Single customer view (SCV), also called a unified customer view, is the process of presenting a single, accurate record for each customer. SCVs allow cross-functional teams and organizations to use aggregated data to drive higher value business outcomes and provide high-quality customer experiences.

Single customer views are imperative for organizations looking to maintain a competitive edge and provide superior customer experiences. In many organizations today, customer data is stored across several different platforms, accessible by individual teams and access is limited or non-existent for many others. This decentralization and inaccessibility leads to missed opportunities, poor customer satisfaction, and lower value business outcomes. 

A 360-degree unified customer view often includes the following data:  

• CRM and customer data
• Behavioral data
• Marketing channel interactions
• Sales representatives’ interactions
• Support tickets
• Project status

Next, we’ll discuss what types of data you’ll want in your single customer view, and the six core benefits you’ll experience once you put yours in place.

What types of data should be in your SCV?

The single customer view is crucial for business success. Data can no longer be stored piecemeal in one platform or another and isolated from the rest of the organization. Instead, data must be shared and instantly accessible across your organization. Single customer views allow you to make critical business decisions with a full picture. The types of data that make the biggest impact are listed below.

Behavioral Data 

Single customer views aggregate a customer’s behavioral data, including website interactions, form submissions, and time on site data (among many other metrics) to allow marketers to make split-second decisions on their audiences’ preferences and buyer’s journey. This data is typically isolated to marketing. With a SCV, sales reps benefit when they know what content piece or pages a prospective customer viewed that prompted them to talk to sales. 

Sales Representatives’ Interactions

SCVs include the most important driver of revenue in a business: sales representatives’ interactions. Pre-sales follow-up and high-quality interactions with customers (driven by background information and superior customer intelligence) are some of the major reasons customers choose one brand over another.   

While most organizations believe they win or lose on price, it’s often service that is the bigger factor, according to recent research from DoubleCheck. In one of DoubleCheck’s recent new win/loss program client onboarding sessions with a midsize enterprise software vendor, the CMO stated, “We felt like the pre-sales support we received wasn’t great, so we didn’t think the solution was worth the extra price.” 

Flaws in pre-sales support can be a major reason C-suite execs or Product Managers often pass on one particular solution in favor of another. SCVs expose this interaction data to your entire organization to create a complete picture of how all teams share in the sales process. For instance, marketing and customer service can get better insight into the sales process when they see phone call frequency/notes, email chains, or in-person interactions (events or quick meetings) that the sales team conducts. Perhaps a customer success rep can better serve a client with a support question if he or she knows that the client is in talks with a sales rep about upselling to the next level of service on your platform.

Customer Service Representatives Interactions

The support tickets that customers submit give insight into how they are using your product or service.  Your technical support team can field hundreds of support tickets per day. Having customer service interaction data at the ready for all teams helps improve understanding across the organization when all teams can see the most common concerns coming in. 

For instance, if a sales team member is on an upsell call with a customer, it’s helpful for that person to know that the client has three open support tickets. Engineering may be interested in seeing what’s causing concern within the user base in real time. Marketing, the team most likely to be monitoring social channels, can benefit from knowing if there are issues with a feature that may come up in social posts.

Marketing Channel Interactions

The number of MarTech applications has risen astronomically in recent years. Chances are, your marketing team has a sizable MarTech stack with tons of valuable information on various platforms. 

Single customer views allow your organization to consolidate fragmented marketing channel interactions into actionable data. It is essential for marketers to clearly understand what is happening across social platforms, SEO tools, ad platforms, SMS tools, marketing automation platforms, and more.

With single customer views, marketers in your organization will no longer be required to waste time and effort logging into multiple platforms to monitor campaign data and conversions. SCVs simplify the work for data analysts by providing a holistic, easy-to-customize view of a customer’s journey across channels, providing a window into the customer’s behavior – tracking their journey from search, to clicks, and ultimately helping you better understand how, when, and why they purchase your product. Better yet, this data is shared across teams like sales and customer success for better communication.

6 Single Customer View Benefits

1. No more data duplication

SCVs eliminate the widespread issue of data duplication across your organization. When data is duplicated, there is a strong likelihood of errors.

2. A better understanding of every single customer

SCVs combine all of the random decentralized fragments of data collected across your organization and then build a true roadmap that clearly presents a holistic view of how your customers interact with your brand, make purchases, and interact with your customer service teams. SCVs allow you to:

• Instantly view each customer interaction and get up to speed on ticket statuses to avoid going into a conversation unprepared or ill-informed. 
• Avoid the frustration of working in standalone service applications.
• Access multiple data points in a single platform – communicate faster, deliver better experiences, and resolve customer issues faster.

3. Improved personalization opportunities

A 360-degree view allows customer service agents and marketers the ability to custom-tailor solutions and interactions as they track each and every touchpoint your customer has with your brand. 

• Understanding your customer’s behaviors allows your organization to build 1:1 relationships and rich, personalized experiences.
• Improved personalization increases brand credibility and enhanced authenticity.
• Better customer experience and more opportunities for customer engagement result from a deeper understanding of customer intent.

4. More efficient marketing campaigns

SCVs allow your team to improve targeting and reach customers at their decision point rather than dragging out marketing campaigns due to poor understanding of each segment’s customer journey. 

• Single customer views ensure better audience segmentation and improve campaign performance.
• Enriched data allows you to improve your ad spend and laser-target your marketing messages to ensure they reach the right audience at the right time.

5. Faster customer service inquiry resolutions

A 360-view of each customer enables you to empower your teams to quickly solve customer challenges.

• Mission-critical customer data is available to all your teams, in real time, empowering them to have more relevant conversations that drive customer satisfaction and success.
• Your team can close tickets and share information across your organization faster than ever.
• Understanding churn rates and churn risk tells you where and when to intervene, re-engaging customers throughout the renewal cycle.

6. A better user experience

Rich user experiences increase the likelihood of future purchases and ensure customer delight across all segments. Delivering world-class experiences will set you apart, drive growth, and improve your brand’s positioning as a leader in your industry. 

Get a 360 degree Unified Customer View in a Unified CRM

Your SCV can be a reality; choosing the right CRM is a first step. Insightly’s Account Plan feature is a great way to set the vision for your SCV. If you’re ready to choose your first CRM switch from your current platform, it’s time to check out Insightly, a unified CRM that combines sales, marketing, project management, and service into one platform to unify your teams. Plus, with Insightly AppConnect, you can build low-code or no-code integrations to connect Insightly with all of the other tools you use throughout your organization. 

Know your customers. Build stronger relationships and unify your fractured data with Insightly’s unified CRM. Schedule a free needs assessment and a demo today.

The post Single Customer View: What it is and why you need it appeared first on Insightly.

According to a recent survey by Gartner, prospects spend 50% of their time seeking information from third-party sources.* Why not study their moves?

What is buyer intent?

Buyer intent looks at aggregated behavioral signals to identify potential prospects in the buying cycle. There are a variety of data points that can represent buying intention.

Intent data can include the following behaviors:

• Web site visits and the frequency of visits
• What specific articles or pages a user is reading
• Which topics seem to interest users most
• Engagement with sales or marketing emails

Essentially, intent data is any type of information that indicates a lead is in the buying phase of their customer journey. The main sources of intent marketing include web traffic, off-site activity, data from your CRM, social media metrics, search intent, and content consumption data.

How is intent data collected?

There are a few types of resources that help businesses capture buyer intent. This includes both internal and external buyer intent tools.

Internal data

Any data that a company collects on its own is considered “internal data.” Also referred to as “first-party” intent data, it is information that is collected in-house using a variety of systems, such as application logs, a marketing automation platform, or customer relationship management (CRM) software.

Most CRMs will display metrics for what visitors are doing on your site. The benefits of collecting internal data include total control of what you capture and how, accuracy, and security.

Additionally, a business can act immediately on internal data and customize exactly what classifies as purchase intent. There’s no waiting for another party to deliver your data. This provides a good way to get started with buyer intent data.

External data

Another way of collecting intent data is through a third-party data collection company. This is typically sourced through cookies or IP lookups on specific websites. Because collected internal data can be complex, external data provides an easier means to the same end.

External data is distinct from internal business intelligence because it is generated by and purchased from outside agencies. This data is used by the purchaser to filter out potential buyers and is even packaged as marketing qualified leads (MQLs).

However, there are some disadvantages to going this route. The company you choose must always be GDPR-compliant. You’ll also need to set up clear expectations and closely monitor the deliverables for consistent accuracy and value of data you purchase.

Data should be sourced from leading industry sites that consumers are using to educate themselves. Research is usually based on search intent, prior buying patterns, and prior buying patterns.

Key indicators of buyer intent

In order to ensure prospects are exclusively a good fit, do your homework. You need to position the segmenting and targeting work around a buyer persona. Otherwise, you could end up with leads who, on the surface, look good, but may never buy a thing.

The first step is deciding what the company values as important and how to score interactions with key decision-makers. You should also account for who is interacting with your brand. There’s no point in spending time tracking and scoring leads that aren’t a good fit.

Key considerations when compiling intent data include:

Recency

How recently has a prospect engaged with the brand? This is super important data. If you wait weeks to contact someone after visiting the site, they probably already made a buying decision.

People waste no time these days and thus, speed matters in sales. The faster you react, the more deals you close.

Frequency

How often are people coming back? The more they return, the more likely they are to buy. If you see a lead frequently viewing pages for pricing or case studies, you can easily assume they are far into the buying cycle. At this point, the sales team needs to reach out.

Engagement

Most lead scoring systems count user engagement. If an individual is engaging with content on your site via chat, email, or other forms of interaction, it’s a good indication they are ready to talk.

How is buyer intent data used?

So, once all of this information is collected, what do you do with it?

You can use buyer intent data in a number of ways. For starters, it’s a key asset for customer acquisition. It works to greatly improve segmenting and targeting of account-based marketing campaigns. Intent data also helps to better align your messaging to buyers’ needs.

Some ways in which you can put buyer intent data to use today include:

Maximize outreach

Intent data gives your sales team a leg up. Sales teams don’t have to wait for buyers to complete an action to identify interest. With simple buyer intent signals, it’s now possible to prioritize outreach based on specific behaviors.

Reduce churn

After the sales team converts a prospect, a business can continue to monitor clients who research the competition. This data points to customers who may need additional support or attention. This usually indicates your product or customer service is failing in some way.

Set up triggers that request buyer feedback to help identify gaps for future product development. Intent data helps to uncover problems before buyers even utter a peep. This reduces the churn rate and adds to overall customer satisfaction.

Guide for messaging

Buyer intent data works to strategically target in-market prospects and convert them to quality leads. This type of data provides insight into prospect research history, including specific products and brands.

Research by Gartner found that “by the end of 2022, more than 70% of B2B marketers will utilize third-party intent data to target prospects or engage groups of buyers in selected accounts.”*

Buyer intent data can be used to better craft unique and specific messaging that speaks to segmented audiences in different ways. Rather than using generic marketing tactics, you can better align your outreach with specific interest signals that buyers leave, such as cookie crumbs across third-party sites.

Pros & cons of buyer intent data

When it comes to using this type of data to conduct business, there are two sides to the coin. Here are some pros and cons:

Pros

Efficient prospecting

For a sales team, closing deals is the top priority. Buyer intent data simplifies prospecting with a layer of business intelligence. Knowing who is looking at what content means you can tailor messaging with more direct targeting.

It also means sales can engage leads as early as possible while collecting information on how and what prospects are researching. Sales will be able to prospect SQLs in a fraction of the time.

Improve outbound sales

This is about working smarter, not harder. The sales cycle can be long. Giving your team direct access to buyer intent data allows them to reach out to the most qualified leads and spend less time on people who aren’t really interested.

It also increases the ROI of your B2B content syndication efforts. See who’s reaching out and target more efficiently.

Sales prioritization

Practice advanced sales prioritization with buyer intent data that lights the way. Traditional lead scoring relies on adding points when certain actions are taken.

Intent data helps to uncover additional avenues a lead takes during the buying cycle. This can be used in a more precise way to predict purchase intent and prioritize contacts.

Personalization & targeting

Intent data helps both the sales and marketing departments to run more accurate account-based campaigns. Successful outreach, including buyer enablement, is built on personalization.

The most effective way to improve B2B campaigns is to provide a continuous stream of relevant content. It allows you to strategically nurture leads by segmenting lists and adjusting the messages accordingly.

Relevancy

When you closely understand consumer problems, you can create more relevant content. Buyer intent data helps to uncover common obstacles and issues people run into that pertain to the product or service you provide.

These insights can be used to better guide content creation and increase inbound leads. Create content that directly reflects exactly what people are interested in and watch the social return on investment skyrocket.

Cons

Accuracy issues

When it comes to purchasing buyer intent data from a third party, there is no true way to confirm the data is accurate. You are simply relying on good faith that the company is giving you correct information.

Leads can be anywhere

Third-party agencies that provide external buyer data include leads that can be anywhere in the funnel. Rather than focus on one buyer stage, most outside sources will send them to you along the entire journey.

That means, purchasing buyer data with the intent of using it for top-of-the-funnel messaging can be risky. Your business is going in directly for the sale when some buyers may just be getting to know you. It comes off as pushy.

Too specific 

Being too specific in targeting can lead a sales team right back to blind targeting. There is such a thing as over-personalization. Zeroing in on super-specific characteristics of a potential buyer can cut out people that are actually willing to buy.

You risk not reaching a wide enough audience and missing out on sales. You should employ critical thinking to determine the fine line between being too general or too specific in your messaging.

Waiting to reach out  

Having sales and marketing wait to respond to buyers reaching out can cost you. Sometimes outbound cold-calling is the best form of gathering new leads.

Non-compliance 

The worst issue a business can run into when purchasing buyer intent data is that it was captured in a way that’s deemed “non-compliant” with the latest data security standards and regulations. Control and management is necessary to ensure the data is being used properly.

If it’s not used correctly, you can face non-compliance with the General Data Protection Regulation (GDPR), which can lead to some hefty fines.

Is buyer intent data worth it?

It all depends on how a business wants to spend its money. Top-of-the-funnel leads require a lot more time and attention. This means fewer leads for your money.

Perhaps a better option is to purchase buyer intent when prospects are part-way down the funnel and use CRM and internal data before that. You can then allocate resources to more profitable endeavors, such as ad spend, customer engagement campaigns, and content creation.

Buyer intent data is most valuable when a business has a well-crafted buyer persona and has the capacity to follow through with leads in a timely manner. A poorly crafted buyer persona or failure to pay attention to details means wasted money on missed opportunities.

Many businesses start targeting before they have fully segmented the audience. Narrow the focus and build out the value prop with relevant content. Then, it will make more sense to purchase buyer intent data. This establishes buyer confidence that your company can solve their biggest problems.

Sources:

*“Emerging Technology Analysis: Leveraging Intent Data for Marketing and Demand Generation.” Alan Antin. Gartner. February 11, 2020.

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Unfortunately, in today’s omnichannel business landscape, creating one map that represents the entire customer journey can seem daunting—if not impossible. After all, some customers are very candid about their motivations and desired outcomes, while others are less willing to open up. Some customers prefer to interact through face-to-face conversations, while others rely on non-verbal forms of communication, such as email, social media, or text message.

With so many personas, goals, motivations, and communication styles to consider, how can you ever develop a single document that represents the customer journey? One way to do it is to start small and develop your customer journey map over time.

Here are three mapping exercises to help you use CRM data in developing different types of customer journeys for your business.

1. Define your ICPs & personas

Customer journey mapping is a waste of time until you have developed a very specific understanding of your ideal customer. Start by clearly defining your ideal customer profiles (ICPs) and personas before spending any time on journey mapping. If you do not have an ICP or personas, consider the following questions:

• If you could only sell to one industry, what would it be?
• Within that industry, what is your primary niche?
• Within your ideal industry and niche, what are the firmographic characteristics of your ideal customer ? (i.e., revenue size, line of business, number of employees, etc.)
• Of the companies that you’ve served in the past, which were less than ideal? Why?
• Who are the types of people (job titles, responsibilities) that your company interacts with?
• Which job titles tend to make decisions about your products or services?
• Which gatekeepers and other stakeholders are involved in the buying process?
• Who will be the actual users or consumers of what you provide?

There’s a lot to think about when developing your ICPs and personas. You may not have all of the answers, and that’s normal. Use your CRM data and build reports that help you answer the tough questions. Your sales team is also a reliable source of first-hand knowledge to help you check your assumptions. Collect all of the feedback and begin simplifying it for the next step.

Example: A manufacturing business that makes and sells assembly line equipment could theoretically have numerous ICPs and personas. However, for customer mapping purposes, it may be beneficial to focus on one industry at a time—especially if buying patterns and customer service requirements vary significantly by industry. Instead of trying to force all industries into a single map, the manufacturing company would be better served to develop one map for automotive, one for healthcare, and so on. The first step would be to itemize each industry’s ICP and persona(s) as follows:

2. Analyze CRM data for closed-won deals within each ICP

Once you’ve defined your primary ICP(s), it’s time to use data from your CRM to identify trends that are common to each journey. Drill down using tags or custom fields and quickly identify won deals that fall within your target ICP. Be sure to set a date range that provides enough meaningful data.

Do you notice any similarities? Things to look for may include:

• Similar interactions in the journey from awareness to close
• Content that was frequently downloaded or viewed on your website
• Marketing emails that helped move deals forward
• Lead sources that were responsible for a sizable percentage of closed deals
• Typical customer buying processes and the personas who were involved
• Objections that were noted during the sales cycle
• Average amount of time that was required to close each deal
• Post-close and implementation details

Note: Looking at closed-lost deals can also be instructive, but you may not need to do it if you have enough closed-won data.

Use actual deal data to build a more complete view of the customer profile. Going back to our previous manufacturing example, the company’s automotive ICP may look something like this:

3. Start building your customer journey map

Having enriched your ICPs and personas with reliable data from your CRM, you’re now ready to begin constructing a basic customer journey map. What’s the best format for your business? There’s no one-size-fits-all template that works for every industry and use case, so here are a few tips for designing a simple, yet effective customer journey map:

Grid layout

Most customer journey maps are built using a graph-based design and have horizontal and vertical axes. Above the grid, it’s important to have your ICP and persona clearly defined. If you’ve developed fictitious personas with names and photos, this might be a great place to use them. Remember, each map should be specific to one persona / ICP combination. If you have several customer journeys to map, start with the most important persona. If certain maps are very similar, you can always combine them or eliminate some later.

Horizontal axis

The horizontal axis of your graph will most likely align with specific stages that customers go through from pre-awareness to satisfied customer. Using your internal sales pipeline terminology could work, although it is better to describe the stages from the perspective of your customer. So, instead of “initial discussions,” you might use the phrase “research vendors.”

Vertical axis

Some customer journey maps try to squeeze as many criteria into the vertical axis as possible. This can lead to an overwhelming experience that defeats the original purpose of mapping. Decide on three to four important criteria as a starting point for your y-axis. Customer actions, customer feelings and thoughts, and common objections are good examples. You can always add more later.

Example customer journey map

Based on our previous example, here’s what a simple customer journey map might look like. You could build this in a document or spreadsheet and hand it off to a designer to pretty up later. The main goal is to get your basic facts on paper as quickly as possible:

Persona: VP Process Engineering – Plastics (Automotive ICP)

Notice that the map’s last row provides space to collect notes and ideas for streamlining each stage of the customer journey. In this example, developing “automated onboarding workflows” is listed as one opportunity to help the customer achieve his or her goal during implementation. Using a tool like Insightly Marketing can be an intuitive and effective way to automate various aspects of the customer journey—from initial awareness to repeat buyer.

Build better customer journeys

As the business world evolves at an even faster pace, smart companies are realizing the importance of building accurate and actionable customer journey maps.

Not sure how to get started? Keep it simple. Rely on data that already exists in your CRM. Focus on business impact rather than worrying about design effects. And, once you’ve built your customer journey map, use it!

To learn more about Insightly Marketing and CRM platform, request a demo.

Request a demo

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If you’re like most marketers, you can rattle off a dozen ways off the top of your head. Ads, click-through rates, blog comments and Facebook likes, and industry research begin to scratch the surface. What about sales calls and customer success touch points? How about their deal size, billing cycle, or even their company holiday card?

To create a full picture of your customer, you have to gain customer insights from every angle. The easiest and most complete way to do this is by using a customer data platform.

What is a customer data platform?

According to the CDP Institute, a customer data platform is packaged software that creates a persistent, unified customer database that is accessible to other systems.

This means:

• A customer data platform must be a single software, not a combination of products.
• A CDP must export a persistent, unified customer database, not a fragmented system.
• A CDP must be accessible to other systems, not a standalone export.

How is a CDP different from a CRM?

But wait, what about your CRM? CRMs, or customer relationship management systems, have become integral to revenue operations. They provide invaluable insight into your customers. Further, they integrate with marketing, customer service, and financial programs. You may even use a unified CRM, which puts many of these integrations into one central system.

Here’s how a customer data platform differs from a customer relationship management system.

CDPs are managed by marketers 

This is unlike a CRM, which is managed by a sales operations manager or a system administrator. A digital marketing manager or analyst can manage a customer data platform.

CDPs are not used to manage customers

A customer data platform is not used to send emails, make calls, or market to customers. Instead, it analyzes things that have already happened.

CDPs go above & beyond the CRM

A customer relationship management system is a great data resource. But, it is not the only data resource. A CPD can enhance CRM data with marketing, service and financial information as well.

Your customer has a story before and after they become a data point. A customer data platform helps your digital marketing team tell that story, without gaps.

What kind of data does a CDP collect?

The customer data platform should collect customer insights at every touch point to provide a 360 degree view. Here are some examples of the types of data a CDP might collect:

Web data

Tools like Google Analytics document your customers’ web behavior. A customer data platform can aggregate links clicked, time on page and bounce rate. Combining this with other customer data can show meaningful trends in user behavior.

Customer identifiers

Your CRM integration will enhance your customer information. With a robust CRM, you can use identifying information to segment customer profiles. Data like names, birthdays and addresses creates fuller pictures of your customers.

Customer histories

Has your customer been around for six years or six months? Are they paying six figures or on a six-week free trial? This sales information, also in your CRM, profiles customers by their purchasing behavior.

Email or social media engagement

Who are the customers who like everything you post on Facebook, but never upgrade? What about those who haven’t opened a marketing email in over a year? These digital marketing analytics are powerful when aggregated with your other customer metrics.

Customer service data

You can learn a lot about a customer when studying how they use your customer service. Combining data from live chats, phone calls, and emails shows customers’ behavior over time and likelihood to churn.

Financial information

At the end of the day, you want to know what customers are purchasing, and for how much. A customer data platform shows lifetime value and total investment into your customers, including acquisition.

Benefits of using a customer data platform

Marketing bridges the gap between your customer and your product. Marketers typically understand their product extremely well. Why shouldn’t you understand your customer at the same level?

Benefits of a customer data platform include:

Campaign optimization to suit your customer profile

A customer data platform shows you which campaigns resonate with your customers. With this information, you can segment your marketing programs to meet customer needs.

Improving your sales cycle

With integrated marketing and sales data, a CDP can identify blockers in your funnel. This information allows you to smoothly move customers from marketing to sales.

Identification of customer correlations

Say you have a hunch that organic customers are more likely to contact customer support. A CDP can prove you right—or wrong. These correlations let you find alignments in your marketing, sales, and support functions.

Reintegration of customer data into other software

Customer data platforms integrate both ways. So, your marketing and sales software can use CDP data to improve your day-to-day functions.

Driving revenue

Better customer understanding = better marketing = better revenue.

Using a customer data platform to improve your marketing

How, exactly, does better customer understanding lead to better marketing? Here’s how a CDP can impact the success of your marketing campaigns.

CDP shows which channels bring in the most revenue

A CDP will draw a direct line between your marketing channel and lifetime revenue. You can invest more in the channels that are high revenue-drivers, and retire those that aren’t. This will increase your marketing ROI and decrease your customer acquisition cost.

CDP helps to improve your product marketing

Product marketing helps your customer better understand your company and product. A CDP uses your data to show customer touchpoints, pain points, and decision cycles. You can incorporate this information into segmented product marketing materials.

Create better segments with complete visibility

We all know digital marketing isn’t one-size-fits-all—it’s meant to be humanized. Our campaigns and programs work best when we tailor them to fit our customers’ needs. With a customer data platform, you can create more specific customer segments. With fewer assumptions, these segments are more accurate, measurable, and effective.

Conclusion

A customer data platform is a great tool to better understand your target audience. The insight into the entire business can be transformative for marketing success. If you are a marketer looking to better understand your customer, consider adding a CDP to your tech stack.

Interested in learning about Insightly’s unified platform for customer data management? Request a demo and see how you can better manage your customer data, align teams, and build stronger customer relationships.

Request a demo

Sources:
4 Types of Customer Data to Enhance Your Marketing Campaigns. Rob FitzGerald. ConnextDigital. June 20, 2019
8 Benefits of a Customer Data Platform (CDP). Jan Hendrik Fleury & Clemens Niekler. Crystalloids. October 7, 2019
Customer Data Platform Basics. Customer Data Platform Institute. 2021

The post What is a customer data platform? appeared first on Insightly.

With the availability and affordability of tools like unified CRM solutions, the measurement of many metrics is automated. Plus, these tools provide dashboards that compile the metrics you need to see in one easy-to-access location.

Unfortunately, many businesses still don’t have a formal metrics and reporting structure established within their organizations. Moreover, those who do report on various metrics, do so in a siloed way and miss opportunities to extract valuable insights and act on them in a timely manner.

Marketing might be reporting on email open rates, but what does that mean for the rest of the business? How does that impact revenue, productivity, customer satisfaction, and company growth? If all marketing does is pat themselves on the back for increasing email open rates, they end up with what are referred to as “vanity metrics.”

Vanity metrics don’t provide much actionable insight. However, certain metrics provide significant insight into business health and drive the smartest growth decisions. We’ll call them “golden metrics.”

• Productivity metrics
• Sales metrics
• Marketing metrics
• Marketing SEO metrics
• Customer metrics

Golden productivity metrics

Internal teams’ productivity levels are key to business growth—that’s common sense. But it’s easy to mistake the activity for productivity. Let’s quickly touch on that before diving into productivity metrics as it’s an important distinction.

Activity vs. productivity: an important distinction

Excessive meetings provide a great case study through which to distinguish activity from productivity.

Let’s say an employee often schedules meetings to discuss X, Y, or Z. However, during those meetings, little is accomplished, no one is engaged, and the information shared could have easily been conveyed through an email.

On the surface, that person may be seen as a proactive and productive colleague who brings people together to drive initiatives forward. But, more often than not, all they are doing is activelywasting time.

With that important distinction out of the way, let’s look at some key productivity metrics you can start measuring today.

Employee experience: The overlooked key to business success

Employee experience (EX) is one of the important variables that dictate employee productivity and a business success. If your employees aren’t happy, inspired, engaged, and motivated (all parts of the overall EX), the quality of their work product will decline. Plus, employees in these states of mind deliver a poor customer experience, further damaging your bottom line.

The challenge of measuring employee experience

It’s challenging to measure EX because there are too many variables involved. Most companies use surveys. But surveys don’t paint an accurate picture of EX for a variety of reasons, key among them are:

1. Many employees are hesitant to answer survey questions honestly for fear of retribution and this skews results. (You may tell them it’s anonymous, but many employees won’t believe you.)
2. Most companies design their own surveys. However, they are rarely designed by psychometric specialists with the expertise to develop an unbiased survey that produces reliable results. For a survey to be effective, it’s best to outsource it to the experts.

Fortunately, you can maintain insight into the quality of the employee experience without using surveys. You do so by analyzing additional metrics that are directly or indirectly connected to EX. These additional metrics, in addition to being an aggregate representation of EX quality, are themselves great ways to measure productivity.

Employee turnover rate

Turnover is natural and happens in every company. But if your turnover rate is significantly higher than industry benchmarks, it’s a strong indication that productivity is down, and your employee experience needs improving. The average national employee turnover rate in the US (as of 2019) was 22%. (1)

What’s the main driver of employee turnover? The graphic below says it all—82% of respondents to a recent survey cited better job opportunities as the leading cause.(2)

Employee engagement

You can measure employee engagement by looking at their usage rates of the technology you provide to make their jobs easier. Participation in employee engagement programs such as employee volunteer initiatives is another way to measure engagement. And you can use surveys, of course. Just be aware of the points mentioned above about using a psychometric expert to design and administer the survey.

Why is employee engagement so important? Consider that disengaged employees in the United States cost businesses between $450–550 billion annually. (3)

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Golden sales metrics

Fortunately for businesses, it’s much easier to measure sales performance than productivity. Below we lay out the golden metrics for sales, many of which can be measured by the powerful reporting productivity tools of a unified CRM.

Sales revenue

Sales revenue is a simple metric to measure and can provide much insight into the health of your business. This metric can tell you how sticky your product or service is, how competitive you are in your market, whether your marketing initiatives are producing results, and a lot more.

Plus, it’s easy to calculate. There are two types of sales revenue: gross revenue and net revenue. While both are easy to calculate, they provide quite different types of insight.

Gross sales revenue

Gross revenue is simply the amount of money your company brings in through sales. If you sell 100 widgets at $10 each, your gross revenue would 100 times 10, which equates to $1,000. That would be your gross revenue.

Net sales revenue

Net revenue considers expenses as well as incoming cash flow. To calculate net revenue, simply take gross revenue and subtract all the expenses in producing and selling that product or service.

For example, let’s say to produce one widget, you pay $1 for parts, $2 for an employee to produce it, and $2 to rent the space and pay the utilities needed to keep your shop open. Your expenses per widget are $1 + $2+ $2, which equals $5. When you subtract that $5 from the $10 in gross revenue you made from selling it, your net revenue would be $5.

What does each tell you?

If gross revenue is increasing, you can ascertain that sales are up. However, if, at the same time, net revenue is dropping, it means the costs of producing and selling your widgets are increasing. And that means less profit for your business. These are important distinctions that inform different types of forward-looking business growth decisions.

Customer acquisition cost

This is another helpful sales metric that sheds helpful light on the effectiveness of your sales team and the overall health of your business.

This golden metric is calculated per month, quarter, and/or year. To calculate customer acquisition cost, start by calculating the amount of money spent on acquiring new customers: marketing spend, sales technology subscriptions, sales team travel costs, etc. in a given time frame.

Next, divide that amount by the number of new customers acquired during that same time and you have your total customer acquisition cost. This metric is best used in tandem with customer lifetime value.

Customer lifetime value

Customer lifetime value (CLV), when used with customer acquisition cost, is one of the most important metrics to measure. In short, it is the total monetary value your average customer brings to your business.

It’s a bit more complicated to calculate. You start by calculating the average value of a single sale for a given time frame—typically one year. For subscription-based businesses, this isn’t limited to average annual subscription cost per customer—you must consider upsell transactions as well.

Once you calculate the average cost per sale, you then multiply it by the average number of purchase transactions you process per year (again, don’t forget to include upsell purchases). Finally, take that number and multiply it by the average customer lifespan (the amount of time the average customer remains a customer before leaving). That’s your CLV. It’s a particularly important business intelligence metric that all businesses should measure. If this is new to you, read this comprehensive piece on customer lifetime value.

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Golden marketing metrics

Let’s turn our attention to marketing metrics and reporting. Metrics are especially important for marketers. Proving the impact of marketing via metrics and reporting is one of the only ways marketers can justify their worth within an organization.

Marketers measure all sorts of metrics—open rates, click-through rates, new leads generated, marketing qualified leads (MQLs), etc. However, many of them don’t shed any light on overall business health and some don’t even help marketers themselves.

Why measuring MQLs isn’t golden

New leads generated and leads qualified don’t mean much because there’s no telling what will happen to them after they are generated and qualified. Many marketers revel in their ability to generate marketing qualified leads (MQLs).

The problem with that metric? The marketers using it to measure their own performance are the same people who define what it means to become “qualified.” It’s a subjective metric that many marketers spend way too much time focusing on and celebrating.

Sales measures lead-to-customer conversion rates (how many leads they convert into customers). It’s a helpful metric for sales teams but it doesn’t tie back to marketing because sales teams find many leads on their own.

MQL-to-customer conversion rate: Where the gold lies

The golden marketing metric in this mix is MQL-to-customer conversion rate, which measures the percentage of MQLs that sales convert into customers. Why is this important? It tells marketers how precise their criteria for qualifying a lead is. You can send MQLs to sales all day, but if only two out of 50 of them convert into customers, you’re not qualifying them properly and should sit down with sales and discuss your lead qualification criteria and revisit your lead disposition process.

What matters is that marketing is sending sales the right leads—those that are sales-ready. Quality wins over quantity here. The MQL-to-customer conversion rate will tell you whether you’re sending the right leads at the right time, or if your process needs to be refined. If your ratio is low, you are qualifying leads too soon. If it’s high, congrats, you should be promoted.

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SEO metrics

Among the most important elements of a successful business in the digital era is a healthy website. It’s crucial that your site can easily be found and is engaging enough for visitors to stay for a bit and return later. To drive traffic to your website, you need to constantly tend to search engine optimization (SEO) tactics—both on-page and technical SEO tactics.

Marketing is typically charged with SEO and website design. In the 80s, the physical brochure was your brand’s public face, and it was incredibly important to make yours shine and stand out from the rest.

Websites are today’s brand brochures and it’s equally if not more important that it shines. Why? There are exponentially more websites today (more competition) than there were brochures in the 80s.

How do you ensure your site’s visibility and high engagement? A few golden metrics will give you a constant sense of how well your site is doing, as well as inform you when something needs to be fixed. Let’s break them down.

Total organic traffic

Organic traffic refers to site visitors that find you via search results. Organic traffic doesn’t include visitors that arrived on your site by clicking an ad result—that’s pay-per-click traffic and is a separate tactic and metric altogether.

It’s easy to measure organic traffic. If you have a website, you can connect it to Google Analytics for free and easily grab loads of real-time data about site health, visitor trends, and more. Logically, you want to see a steady uptrend in traffic per week and month over time. Ideally, you want your traffic chart to resemble Berkshire Hathaway’s stock share price chart.

How to interpret organic traffic

You’ll see traffic dips here and there, but you should expect to see more and more visitors to your site as you grow. If your traffic plateaus, it could be caused by any number of things, including backend technical SEO issues that Google and other search engines see as negative factors and penalize your site’s ranking for.

The other usual culprit that causes traffic to stop growing is a drop in the quality of your content. Google’s algorithm keeps getting smarter and can increasingly differentiate high-quality content from fluff and clickbaits. But content quality is better measured by the next metric on our list: average session duration.

Average session duration

This metric can also be pulled from Google Analytics. It tells you the average amount of time visitors spend on your site. If this metric is hovering around one minute, it’s an indication that your site is not engaging visitors and needs some work. If session duration is, on average, three minutes or above, you’re looking good. When you reach five minutes, it’s time to bring out the champagne.

Bounce rate

We’re still in Google Analytics with this one. A “bounce” refers to a visitor who lands on a page, takes no action such as scrolling, clicking anything, etc., then leaves. In other words, they did nothing on your site. They came, took a peek, didn’t like what they saw, and left.

Alternatively, it wasn’t that they didn’t like what they saw but rather they realized they were in the wrong place. That results from your site’s rankings not aligning with search intent, a topic that’s broad enough to deserve its own article.

Quality backlinks

Backlinks are links on other sites that reference information on your site, then link to and send their visitors to your site to learn more. Google sees this as a powerful sign that your site is authoritative and thus ranks it higher.

Backlinks are an important metric to track but beware of one tempering yet self-destructive temptation. Don’t purchase backlinks. You must generate them organically by publishing amazing content that people want to consume. If you buy backlinks, anyone who clicks on them is likely to bounce and/or skew your other website metrics in a negative direction. Also, make sure that your backlinks are from high-ranking, relevant, and quality sites, otherwise they’ll affect your SEO negatively.

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Golden customer-focused metrics

We round out our guide to golden business intelligence metrics with some important customer-focused key performance indicators (KPIs). We’re now in the age of the consumer and customer expectations are higher than ever before. Catering to customers’ needs has never been more important. The metrics below are vital to maintaining a healthy business and insight into your future growth trajectory.

Customer churn and retention

These are two separate metrics but tie into one another. They provide the same type of insight but from opposite ends of a spectrum.

Customer churn

Calculated as a percentage, customer churn rate is the proportion of customers that you lose in a given month or year. It’s a great metric for keeping an eye on how quickly your business is growing and reflects the performance of every team in your business. It’s particularly helpful for subscription-based businesses.

It tells you if you’re losing more customers than you acquire and vice versa. Churn rate is easy to calculate. Simply take the number of customers you lost during a given time frame and divide that by the number of customers you had at the beginning of that timeframe. Then represent that number as a percentage.

For example, if you started the year with 100 customers but lost 15 that year, you would divide 15 by 100, which equals 0.15. As a percentage, that’s 15%. So, your customer churn rate would be 15%.

Customer retention

Customer retention is also most helpful for subscription-based businesses. Customer churn shows you one side of the coin while retention shows you the other. Customer retention tells you the percentage of customers who stick with you and renew their subscription to your product or service.

To calculate retention, you need three numbers: the number of customers you started the year with (A), the number you acquired during the year (B), and the number of customers you had at the end of the year (C). The formula looks like this: ((C – B) / A)) x 100.

For example, let’s say you started the year with 100 customers, acquired 20 new ones, and ended the year with 110 (because you lost 10 during the year). You could subtract 20 from 110 and have 90. Then you’d divide 90 by 100 (the number of customers you started the year with) which gives you 0.9.

Viewed as a percentage, 0.9 is 90%, which would be your customer retention rate. Now, is 90% a good retention rate? That depends on your business model. However, in most cases, it’s a high retention rate that means your business is stable with reliable recurring revenue. If this metric is new to you, learn more about customer retention and strategies to keep your rate high.

Customer effort score

Customer effort score (CES) is a simple metric that measures customer satisfaction and customer experience at the same time. There are various customer satisfaction metrics out there. Many businesses rely on net promoter score (NPS) as the holy grail of satisfaction metrics. However, using NPS as an end goal is misleading both for employees and businesses. NPS should be used as a beginning point, a way to learn and track customer satisfaction for ongoing improvements and building better customer relations.

Now, back to CES. CES measures the amount of effort a customer had to put into a specific interaction with a company. Many businesses use CES to assess the effectiveness of their customer support function, but you can use it to measure any interaction your business has with a customer.

In many ways, higher levels of customer satisfaction depend on reducing the effort a customer must put forth when interacting with a business. If their issue can be solved in a few minutes without putting much of the burden on their shoulders, they will come away satisfied. That indicates a satisfied customer who just received a positive customer experience. Checkmate.

CES tends to be more reliable than other satisfaction metrics. CES is calculated by asking customers to rate the amount of effort they had to put into an interaction, on a 5-point scale, with 1 being “very low effort,” and 5 being “very high effort.”

Collect a number of scores and calculate the average. A score of 2 or lower means that a company is making life easy on its customers, and they are happy. A score of 4 or 5 means that the company should rethink how they support their customers with a mind towards taking some of the burden off their shoulders.

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Connecting the dots

We just covered some of the most valuable metrics businesses today—metrics that provide real, actionable insight. That insight is necessary to make data-driven decisions today.

It’s important that your leadership team gets behind business intelligence and reporting efforts. After all, we have the data at our fingertips. Why would we not use it to inform the decisions we make that will dictate the future survival or failure of our businesses?

Good luck, and may the data be with you.

If you’d like to learn how Insightly CRM can help you to align teams around key metrics, reduce data silos, and create a data-driven culture and decision-making, then request a free demo.

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Sources:

1-2. North American employee turnover: trends and effects, Mercer, 2020

3. DNA of Engagement: How Organizations Can Foster Employee Ownership of Engagement, The Engagement Institute, 2017

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In today’s business environment, where more people are working remote than ever before, businesses need a way to centralize customer data and make it accessible on the go, from anywhere. For nearly every company, this means a CRM platform with a mobile app.

According to a recent Mobile CRM Market report by Future Market Insights, modern businesses are placing customer satisfaction at the top of their priority list and are actively seeking fine-grained insights for their sales realms to access on the go. This, in turn, is preparing the grounds for high-scale adoption of mobile CRM platforms across the globe through 2029.

While accessibility from any device at any time is a powerful advantage, it requires added security to protect the valuable information housed in your CRM. When properly managed, CRM data is an important asset to your business. So as you consider introducing mobile CRM use to your team’s daily tech stack, make sure both your CRM provider and your team understand and meet data security and customer data privacy standards. It’s important to your customers and it should be important to you.

Here are the top four mobile security questions to ask your CRM provider.

1. How do you manage mobile CRM security?

Start with the basics. Make sure your CRM uses the same high security standards across all applications, including mobile. Check to see if your CRM has been independently assessed for compliance to SOC 2 Type 2 for secure data management and customer privacy. Double-check the company’s compliance with EU-U.S. Privacy Shield and the General Date Protection Regulation (GDPR).

2. Is there an option for custom user permissions?

This may seem pretty straightforward at first, but access is about more than just the number of users on your platform. If your CRM provider uses unified endpoint security solutions, then you’ll be able to control user permissions and role-based access to CRM data consistently across all devices. It’s great to bring cross-functional teams together using a single source of truth on customer data, but not every person in your organization needs access to all of the details of every account and project. Make sure your mobile CRM app can capture different user roles with the added security, so that each role only sees the information that is relevant to their work.

3. What are ongoing proactive security measures?

While adherence to established data management and customer privacy standards is non-negotiable, you want a CRM provider to continuously improve overall security rather than just respond to malicious threats. If you are in a highly regulated vertical, like finance or healthcare, you may need custom security solutions and ongoing maintenance.

4. Are there any safeguards against employee oversight?

While a mobile CRM offers freedom, convenience, and speed, it comes with certain risks. Your mobile CRM can be used as an entry point into your core CRM system. Phones and tablets get stolen or lost. So in addition to user permissions management, double-check that your mobile CRM has robust user authentication and end-to-end encryption. On your part, include mobile CRM usage best practices in your internal customer data management and user guidelines.

Depending on your industry, you may need a CRM provider with custom security solutions. There are also a few auxiliary things to consider, such as user interface (UI) and integrations, that can impact a mobile CRM’s security. Even though modern CRMs are able to balance user interface with security measures without sacrificing the quality of either, make sure that your mobile CRM app was designed for security, not just to please the eye.

It takes a lot to keep data safe, secure, and accurate. It takes only one data leak or data breach to ruin a company’s reputation and hard-earned trust with the customer. Take your mobile CRM security seriously and you’ll set up your team and business for long-term success.

Read more like this:

• What is data integrity? How to achieve it?
• What are different types of customer data?
• How to organize customer data in a CRM

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Data is objective, unbiased information. Data-driven decision making is the process of studying large amounts of data, analyzing it to identify patterns, obtaining actionable insights, and using that insight to make business decisions.

Intuition is subjective, and business decisions should be made based on objective information. Intuition is effective when you don’t have data or the time to think logically before making a decision. And even though you can develop intuition based on knowledge and experience (a type of data), it’s still risky to use it in business decision making.

Experts suggest relying on data, not intuition

Experts almost unanimously agree that data-driven decision making is more reliable than intuition-based decision making. Cold, hard numbers are simply more dependable than intuition.

Nobel Prize laureate, Daniel Kahneman, says humans formed intuition as a tool to alert us to potential risks. It aids in our survival when we’re faced with fight or flight situations. However, Kahneman claims that using data to make decisions is critically important because it decreases our propensity to make poor ones.

Nevertheless, reliance on intuition is still widespread in business today. Let’s dig deeper to understand why.

Challenges in data-driven decision making

Businesses have the data at their fingertips, but how do they organize it in a logical way? Many still struggle to understand how data is used to make decisions.

Too much data for the human mind to analyze

There is so much data in the world today that it would take over 180 million years to download it. That poses a challenge for businesses because the human mind can’t analyze terabytes of data on its own. Gartner sums this challenge up perfectly, “Organizations have access to unprecedented volumes and variety of data, but deriving insights continues to be a struggle.” (1)

Struggling to effectively leverage data

A 2019 survey asked CEOs which factors impeded their ability to leverage data to make more informed decisions. Surprisingly, 54% cited a lack of data-driven skills and analytical talent. Additionally, 51% blamed data silos, and 50% pointed to unreliable data.(2)

Rampant skepticism around data accessibility & reliability

Humans can’t process and analyze so much data manually, but technology can. Many successful companies comfortably rely on customer relationship management (CRM) software to crunch the numbers.

Regrettably, many leaders either don’t trust their data or haven’t adopted the technology to analyze it. A recent Deloitte survey revealed that 67% of business decision makers aren’t comfortable basing decisions on data pulled from their current technology.(3)

What’s worse, another survey uncovered that 53% of senior executives feel they are too old to learn data analysis skills.(4) Yet, being an effective business leader increasingly requires data analysis skills. This is forcing a shift in thinking about data, all the way from the C-suite down to entry-level employees.

Considering the statistics above, how can businesses successfully transition to leveraging a data-driven approach to decision making?

How to become a data-driven company

Given the importance of data-based decision making, businesses must first understand the benefits involved. Then they must learn how data is used to make decisions and implement measures to begin a company-wide transformation.

Lead the way

When CEOs champion data-driven business cultures, performance results and revenue increase. The Deloitte survey illustrates that when CEOs lead the charge, businesses are 77% more likely to significantly exceed business goals. Plus, they are 59% more likely to gain new insights from the metrics they track and to use data analysis to drive business decisions. (5)

Create a company culture that supports data-driven analytics

Only 13% of businesses claim to have the proper culture, technology, and skills to support data-driven decision-making.(6) That doesn’t mean it’s impossible.

Identify “data champions” who can lead change across your entire organization. You can also hire a chief data officer to work closely with the C-suite on designing and implementing initiatives that foster a data-driven culture.

Gaining buy-in from your sales director is crucial. This will also create a trickle-down effect across the entire sales organization. The majority of data that decision makers want to see comes from sales. If the sales director enforces consistent data management, her team will be more likely to fall in line.

Use the right technology

Many (perhaps most) companies today use CRM software to capture and analyze decision data. CRM technology has evolved and businesses can now leverage unified CRMs that include tools to measure sales and marketing performance.

This delivers three key benefits. First, when all your data is stored in the same system, decision makers find it much easier to rely on the data and analysis these systems produce. Second, when you have all those functions in one system, you reduce software costs while giving everyone in your company access to decision-making data. Third, all your teams are better aligned with one centralized source of truth on customer data. Without the right technology, creating a data-driven business culture is exponentially more difficult.

Look to younger generations for support

Younger employees are more comfortable with data than “analog” generations. It makes complete sense—they grew up in the digital era. We should turn to younger decision makers for support in championing the transition to a data-driven business culture.

Plus, younger generations are more willing to embrace change. In fact, 76% of executives in their 30s or younger look for opportunities to leverage new technology to achieve business goals. Plus, 67% of them see risk as opportunity, not danger. (7)

In the coming years, we can expect to see more young people in decision-making roles. They will employ more technology—like CRMs—to capture and analyze data for decision making. Why? Because they more clearly recognize the vast array of benefits gained from CRMs and data analysis technology.

What does the future hold?

Increasingly more businesses are adopting a data-driven approach to decision making. But it may be a few years before this trend starts to dominate business. We still see laggards across industries despite the data that indicates businesses grow faster and outperform their peers when they leverage data to drive decision-making. As competition increases in the digital economy, using data insights won’t be a matter of preference, but rather of necessity.

Read more like this:

• 4 steps to building a unified business approach in 2020
• What are different types of customer data?
• How to improve customer experiences using data analytics
• What is data integrity? How to achieve it?

Sources:

1. “Tie Your Data and Analytics Initiatives to Stakeholders and Their Business Goals,” Gartner, 2020
2. “22nd Annual Global CEO Survey,” PricewaterhouseCoopers, 2019
3. “Analytics and AI-driven enterprises thrive in the Age of With,” Deloitte, 2019
4. “The State of Dark Data,” Splunk, 2019
5. “Analytics and AI-driven enterprises thrive in the Age of With,” Deloitte, 2019
6. “Data-Driven Mindset Report,” Mention, 2019
7. “How Younger Generations are Reshaping the Future Workforce,” Inavero, 2019

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We have the data, but we can’t rely on it because…

How often do staff make statements like this? If your company struggles with data integrity issues, it may be a more common occurrence than you think.

With today’s rapid pace of change and uncertain market conditions, staying ahead of data challenges can feel insurmountable. As a result, many businesses stop trying altogether and settle for data that’s “good enough.”

So, what’s your next move?

Is “good enough” data actually good enough? Or, is it time to take a more proactive approach toward achieving data integrity? If you’re tired of unreliable or downright bad data, the following tips should set you on a better path.

Achieving data integrity

As the old adage goes, “A journey of a thousand miles begins with a single step.” Achieving data integrity may seem like a massive undertaking, but getting started doesn’t have to be complicated.

1. Assess your greatest threats to data integrity

You can’t fix something unless you realize that it’s broken. Sure, your people are quick to express their skepticism about your data. But skepticism is a symptom of a much deeper problem. Start asking “why” questions and get to the root of your data integrity issues.

For example, why doesn’t your marketing team trust the open and click-through data in your email marketing system? Does a setting just need to be adjusted, or is the software incapable of providing better data? What do your users think? Dig deeper.

While you’re at it, ask team members to share any other data integrity challenges that they’re struggling to solve. Engage users to understand how data limitations inhibit operations and stifle decision-making. As new issues are surfaced, add cards to your data integrity kanban project board for future sequencing. Then, focus your efforts on the data integrity issues that, if solved, deliver the greatest impact for the least amount of effort and resources.

2. Look at your data more often

Data’s impact to your company is significantly reduced when it’s underutilized. Surprisingly, many companies make the mistake of rarely (or only occasionally) using data to inform their decisions.

Your CRM is, of course, an excellent platform for measuring sales output and pipeline value. But what about all of the other business intelligence data that goes unused? How often does someone within your organization review (and use) other data-driven insights housed within your CRM, such as:

• Project and task throughput
• Productivity by team member
• MQL-to-SQL ratios
• User activity in the system (or lack thereof)
• Customer lifecycle data
• Web-to-lead trends

Incorporating more data into your organization creates a virtuous cycle that naturally elevates data integrity. As data begins to influence more decisions, the desire for greater data integrity organically increases across the board. Your people will begin to ask their own “why” questions and seek out ways to overcome roadblocks.

3. Staff up with data experts

Not everyone in your organization is destined to be a data scientist, and that’s OK. As data becomes an integral part of your daily operations, however, you may need to bring on additional data-minded people to solve complex data integrity problems.

Recently, I was feeling frustrated by a client’s advertising campaign that seemed to be underperforming. No matter how I sliced and diced the data, my web analytics reports kept showing a low conversion rate—despite anecdotal client feedback that seemed fairly positive. After partnering with a data analytics expert to perform an in-depth review, we realized that a poorly written regular expression had artificially deflated my lead conversion data. In short, we had a data integrity issue that was only identifiable (and fixable) by working with a data expert.

So, who at your organization is a “data expert”?

If you don’t have any, now’s the time to start looking for at least a fractional resource.

4. Build data integrity into your way of doing things

Whether they’ll admit it or not, your users can be a major cause of data integrity issues. Errant data uploads, accidental record deletions, bad habits, and laziness can quickly erode the reliability of your corporate data.

As with any company-wide initiative, maintaining data integrity should be hardwired into your organization’s way of doing things. Here are a few ways to infuse data integrity best practices into your company culture:

• Limit user access to only the systems and permissions they need to perform their jobs
• Create work instructions and operating procedures for collecting and modifying data
• Use out-of-the-box system functionality and limit custom fields whenever possible
• Integrate data integrity into your onboarding process for new hires
• Host regular training sessions to keep staff informed and committed
• Establish clearly defined roles so everyone knows who is in charge of data uploads and integrations
• Regularly solicit feedback from users to understand their data challenges
• Make it easy for users to request custom BI dashboards and reports (if they are not permitted to build their own)
• Identify and monitor data integrity metrics to proactively resolve issues
• Appoint a data integrity manager, who will own data integrity at your organization

5. Simplify when possible

Maintaining data integrity is especially difficult when silos exist or integrations keep breaking. As cloud-native solutions continue to advance and evolve, you may be surprised by the amount of system overlap in your tech stack.

Look for ways to simplify your data infrastructure. Do you really need a separate system to send out your monthly newsletter when your CRM offers the same functionality? Could a unified system be more cost-effective—and better from a data integrity standpoint, too?

Ask tough questions. Challenge the status quo. Be an advocate for data integrity.

Data integrity as part of your strategy

No doubt, data will continue to play an increasing role in every aspect of business. Therefore, smart organizations remain laser-focused on maximizing data integrity. And, as we’ve explored in this post, achieving data integrity is not a “check the box” proposition. It takes a long-term perspective and a commitment from every user and team.

Looking for more real-world examples? Check out this data management guide to see how six midsize companies leveraged modern technology and processes to overcome their data integrity issues.

Ready to explore a unified CRM that can help you better manage customer data throughout the entire customer journey? Request a free demo and needs assessment with the Insightly team.

Request a demo

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In this post, we’ll take a deeper look at customer data and explore best practices for customer data management.

4 primary types of customer data

Names and job titles. Email addresses. Support ticket records. Online reviews. Transactions. Cross-device usage patterns.

The list goes on and on…

Simply put, customer data comes in many shapes and sizes—and, from many sources, too. Making sense of customer data can seem overwhelming, especially without the right perspective. As a result, some companies fail to overcome roadblocks to effective data use, thereby diminishing the impact of their most valuable business asset.

To create some semblance of structure and clarity, customer data is often categorized into groups. Here are four primary customer data groups.

1. Basic data

Basic personal customer data forms your organization’s fundamental understanding of each relationship. Many—if not most—standard data fields in a CRM could be considered basic data. A contact’s name, email address, phone number, job title, and linked organizations are examples of basic customer data. Demographic data, such as gender and income, or firmographic data, such as annual revenue or industry, are also basic customer data.

When aggregated and analyzed across multiple contacts and/or organization records, basic data builds the basis for audience segmentation. Then, by using tags or reports in your CRM, you can begin to visualize how many customers share common attributes.

2. Interaction data

Sometimes referred to as “engagement” data, “interaction” data includes the many touchpoints that customers have with your brand. Interaction data is particularly useful for informing decisions that pertain to the buyer journey. Pageviews, ebook downloads, social shares, email inquiries, and demo requests are common examples.

Interaction data is often anonymized and aggregated for high-level reporting purposes (with the ability to “drill down” for further insights). For example, marketing consultants spend considerable time studying interaction data in web analytics platforms to understand campaign effectiveness and return on advertising spend (ROAS). In addition, some marketing platforms provide user-level reporting to track where each customer came from.

3. Behavioral data

“Behavioral” data offers insight into the customer’s experience with your actual product or service. (Note: The difference between interaction and behavioral data can seem fairly nuanced depending on your business and industry.)

Technology companies are frequently cited as premier users of behavioral data, such as free trial sign ups, user account logins, feature utilization, user license additions, deactivations, and downgrades.

That being said, almost every organization maintains some type of behavioral data (even if they do not realize it). If you’re a service-based company, you probably send detailed invoices that inform customers about why they’re being charged. Why not leverage this data to hone in on your most popular solutions? If you’re a manufacturer, you regularly receive purchase orders that are tracked in your ERP. In addition to helping you accurately fulfill your customer’s request, each purchase order represents an excellent opportunity to understand customer preference and identify future trends.

4. Attitudinal data

“Attitudinal” data helps you understand what customers think about your company and the solutions that you provide. Unlike the other three types of data, attitudinal data delivers a first-hand account of what customers actually think. Online reviews, support ticket comments, and satisfaction surveys are sources of attitudinal data.

Here’s the big problem with attitudinal data: Some customers are louder than others when it comes to expressing their opinions about your company. Does one scathing review from a dissatisfied person truly reflect the sentiment of your entire customer base? Probably not. That’s why consistent and proactive collection of attitudinal data from a statistically significant group of customers is key.

Collecting and managing customer data

Once your team has established a firm grasp of these four primary types of customer data, they can begin discussing how best to collect it. Here are a few questions to think through as you formulate your data collection and management plan.

What are our data goals?

On its own, data offers minimal value to your organization. Start with the big picture. Do you want to harness data to improve customer experience or develop new products or features? Is accelerating revenue growth or profit maximization the primary motivator? Discuss and agree on your “why” before getting bogged down in the minutiae of data talk.

How do other companies in our industry leverage customer data?

You’re probably not the first company in your industry to seek a more data-driven culture. Research how similar organizations have leveraged customer data in a secure and scalable way. What are their data goals? Supplement your planning efforts with your findings.

What data is essential?

Accessing and integrating customer data requires effort and, likely, an upfront opportunity cost. As a result, you may not be able to have all of the data that you want on day one. What data is critical to the current and future health of your business? How does it align with your stated data goals? Sequence the most essential data first and build a backlog of secondary data to revisit in the future.

What is the cost of accessing and managing data?

In addition to intangible opportunity costs, you may also encounter tangible expenses, such as software and consulting fees. Does your current CRM offer the right mix of integrations to properly ingest and report on your essential data? Does your CRM vendor charge a premium for AI-driven features that make predictive analytics a reality? If you do not already have a CRM, what is the cost to evaluate and implement a system from the ground up?

Do we have the right tech stack?

Sometimes less is more. Trying to integrate multiple systems is more work than just starting over with a unified platform that does everything you need.

Case in point, I have one client who is thinking about switching CRMs. For years they’ve relied on separate CRM and marketing systems, creating constant confusion in the customer data management process. Insightly Marketing, which consolidates CRM and marketing under one platform, could be a viable solution for simplifying and elevating their customer data.

What are the risks?

Data protection regulations (like GDPR) along with industry-specific data security requirements are becoming commonplace amidst our data-centric economy. Understanding security risks and implementing safeguards to protect customer information are key steps for any company that wishes to use data.

How will we maintain clean data?

No one wants bad data. Without the right systems and processes, however, bad data will become an all-too-familiar reality. To keep data clean, implement proactive data deduplication measures, keep your staff well-trained, and look for opportunities to eliminate manual data entry.

What is our expected (and actual) ROI?

Anything worth doing should be done the right way. The use of customer data is no exception. If your primary goal is to reduce churn, put a hard number on it. For example, “We expect to reduce churn by 10% by effectively using customer data.” Once agreed to, set a process to measure progress toward the achievement of this goal.

Maximizing data’s impact

It’s clear that customer data is more important than ever before.

With the right mix of tools, systems, and processes, your organization will put itself in a position to effectively use customer data and, ultimately, achieve more goals and grow business.

How are you managing your customer data? What are the systems and processes you use to help you make the most of your customer insights?

You can get a free needs assessment from Insightly and discuss best solutions for your immediate and long-term customer data management goals. Request a demo—it’s free and no commitment is required.

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It happens to everyone. But why?

Let’s explore how bad CRM data happens to good teams—and how to fix and prevent it.

What is “bad data” and why does it happen?

What exactly is “bad data” in the context of your CRM? Defining bad data can seem like a rather subjective exercise. That being said, from a marketing consultant perspective, bad CRM data usually comes in one of the following forms:

Errant records

Example: A lead record contains the wrong email address and/or phone number.

Incomplete records

Example: An organization record contains zero relationship links to related contacts.

Inconsistent records

Example: Some lead records use proper capitalization, while others are all lower case.

Overlapping records

Example: One customer relationship is being tracked across duplicate contact or organization records.

Valueless records

Example: Your company runs an online ad campaign that generates dozens of non-business email addresses that are clearly fakes.

As illustrated by the previous examples, bad data does not originate from a single source. Rather, bad data usually creeps in over time as a result of lax business processes, broken systems and integrations, and subpar decision-making. In short, bad data comes in many forms and from many places, which is why fixing it can be such a challenge.

Fixing bad CRM data

There’s not a magical solution for fixing bad CRM data. There are, however, several steps that you can take to improve your situation:

1. Identify good data and its sources

Before you get bogged down with negativity, it may be wise to first identify the success stories in your CRM. For example, is there a specific advertising campaign that consistently produces large amounts of highly qualified leads with zero (or minimal) data discrepancies? Or, perhaps the use of drop-down menus has streamlined data entry and minimized oversights. Carefully study what works and plan to do it more in the future.

2. Identify and fix largest sources of bad data

We’ve already established that bad data comes from countless sources. But, is it possible that a few sources are responsible for the largest chunk of your problems? If you struggle with duplicate records, perhaps your web-to-lead integration is misconfigured and requires an adjustment. Or, perhaps your CRM administrator does not understand how to properly import trade show attendee lists and could benefit from additional training. Spend time investigating the situation and look for easy fixes that could eliminate hundreds or thousands of issues with minimal effort.

3. Identify and fix less frequent sources of bad data

After correcting the largest issues, it’s time to move on and address the myriad of other less obvious causes of bad CRM data. Here are just a few examples and fixes:

Sales reps don’t have time to worry about entering clean data: Sales reps are very busy people and not everyone is always “detail-oriented.” On the other hand, good data is essential for modern sales teams. Data integrations can simplify the collection of key business information, thereby freeing up sales staff to focus on what they do best—sell.

Customizations are out of control: There are many stakeholders to keep happy when your CRM is your central source of truth. Sales reps want to know everything possible about their leads and contacts. Support agents need a way to track customer satisfaction and prevent churn. Accounting wants the ability to flag problematic accounts. These wants and needs can often manifest themselves as CRM customizations, which can clutter your CRM with unused or misused data fields. Unused and misused data fields lead to bad data. Therefore, be strategic and selective when agreeing to implement a stakeholder’s request for CRM customization. Consider all possible use cases of a customization feature and find a solution that will stand the test of time.

Lack of structure for free-form text fields: VP of marketing. VP Marketing. Vice President, Marketing. These variants essentially mean the same thing, but, when expressed differently, can create confusion and muddy your reporting and segmentation data. Look for ways to standardize the assignment of job titles or consider using tags to categorize contacts by persona.

Deduplication is too complicated (or risky) to mess with: Deduplication can seem like a scary thing, especially when you do not have a formalized lead disposition process. In reality, deduplication prevents staff from wasting time by keeping your data clean. Deduplication workflows vary by CRM provider, but, if you’re an Insightly user, be sure to check out the SmartMerge guide.

No one is validating the data: Banks hire auditors to ensure their financial data is in compliance. Your CPA reviews your business and personal financials to help you file an accurate tax return. But, who is auditing your most valuable business asset, i.e. your CRM data? Ongoing data validation in your CRM is a key step for maximizing business insights and identifying new sources of bad data.

Preventing bad CRM data

In addition to ongoing data validation processes, what other steps can you take to prevent bad CRM data? Here are a few ideas:

Rely on data-driven indicators, such as MQL-to-SQL: What percent of your MQLs (marketing qualified leads) are actually accepted by sales? If the number is low or on a downward trajectory, you may be dealing with an underlying data issue. Does sales have the information it needs to accept a certain type of lead? Is marketing attracting leads that are in the wrong stage of the buyer journey? Sales and marketing metrics, such as MQL-to-SQL ratios, can serve as leading indicators of data-related issues before they become bigger problems. Check your lead disposition process. Use Insightly’s quick guide to learn how to set up and/or improve your lead disposition.

Leverage AI in your CRM: It’s easy to get overly excited about your CRM’s sales and marketing features at the expense of lesser-known technical capabilities. For example, Insightly users will be glad to know that their CRM automatically checks for duplicates each time contacts are added or a data import is performed. Be sure to fully understand and use your CRM’s duplication prevention safeguards.

Frequently reinforce data cleanliness: Your teams can play a pivotal role in maintaining data cleanliness, but only if they understand how to do so. Create standard operating procedures for entering data into your CRM and distribute them to staff. Keep these procedures up-to-date and require managers to ensure their staff have read and understand them. When you hire new staff, make the CRM data management training a mandatory part of onboarding. Look for other ways to make data and data cleanliness an essential part of your company culture.

Out with the bad

No doubt, bad CRM data is a real problem for businesses of all sizes. However, with the right approach, it’s a problem that can and should be resolved. By proactively rooting out bad data and implementing sound business processes, you and your team can maximize the usefulness of a CRM and, as a result, make better decisions, align teams, and grow business faster.

Learn more about the importance of proper customer data management and pick up a few best practices from Insightly’s data management blog series:

• Why customer data is increasingly important (Part 1)
• Top 3 roadblocks to effective data use (Part 2)
• How to accelerate revenue growth with data (Part 3)

To learn how Insightly CRM can help you solve your customer data management needs, request a demo.

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