Create a CRM Analytics Strategy – 5 steps to Information Nirvana
- Customer Relationship Management analytics drive improvements to customer interactions and company decision making. Even small improvements are shown to deliver significant financial impact.
- A CRM analytics strategy must show how to connect data, insights, action and outcomes.
- Speed and quality of decision making used to be contrary. But no longer if you correctly apply analytics strategy and technology.
CRM analytics are customer-centric analytics, in fact they are often called customer analytics.
The goal is to get the right information to the right people at the right time, so they improve customer experiences and make more informed and better decisions. But as you might expect, achieving better decision making by more people is a tall order.
An information strategy is needed to identify and prioritize what information is required for each user role and engineer CRM software to route that information where and when it can be applied. It's not a one and done exercise so the strategy also becomes your roadmap.
Here is the Johnny Grow 5 step CRM analytics framework to achieve the goal of more timely and better decision making by more people in the company.
First answer the question, what information needs to get to what person and when to make an improved business decision? The best way to do this is with a Design Thinking workshop.
Not all information is equal, so it should be weighted and prioritized. Apply user stories or use cases to describe, weight, catalogue and prioritize information. If you follow the user story format – As a (user role) I need (capability) to achieve (benefit or result) – your work can then merge into a Product Backlog for phased execution. The key here is that all information should be categorized by role, type, value, cost and payback.
Define the source data and how to convert it into information, and then insights.
Identify the sources of data, capture frequency or scheduling (i.e., real-time event-based (synchronous) updates or batch-based processing), any data transformation requirements and the distribution or routing to get it to the right person.
This is best done with an architecture diagram. It's likely that some data such as customer purchases or other transactions reside outside the CRM system so you will also need to specify integration requirements to harvest and consolidate data.
Most information is just grouped data. That's a start but it needs to be further matured into insights. Recognize that insights are not data, facts or statistics; these are all information. Insights are the reasons, behaviors or learning behind the data, facts or statistics. The dictionary definition of the word insight is seeing below the surface. It's something that teaches and makes users take notice.
Historically, where analytics existed within a company, they often fell under the purview of IT and finance leadership and were mostly directed to delivering insights that remove costs. However, as customers have become more connected, informed, empowered and demanding, the balance of power in commerce has changed, and forward-looking brands are responding with revenue insights that focus on how to better acquire, grow and retain customers.
These types of insights show how different types of customers (personas and customer segments) begin their purchase process, advance their purchase journey and make their purchase decisions. They identify the company's Ideal Customer Profile (ICP) and create precision campaigns, offers and other messaging to engage customers.
They also show how customers use and consume the company's products. This improves product or service quality, aids innovation and allows timely action, just when its needed, to increase customer share and retention.
The third step is to select your analytics toolset.
This is generally done using the analytics tools in your CRM software and includes lists, views, packaged reports, ad hoc or custom reports, dashboards, predictive analytics, exception reporting (such as notification alerts) and Artificial Intelligence. Other popular tools outside your CRM software include data lakes and data warehouses.
Reporting tools are a lot like other tools in that the right tool will make the job much easier and more effective. Not all tools are reporting tools. For example, enterprise social networks such as Salesforce Slack or Microsoft Teams may be the best means for certain types of information delivery. Some business intelligence tools deliver more powerful results. The Analytics Continuum shows how different analytics tools deliver different value.
The next step is to determine how to make the insights actionable to achieve an outcome.
Information is often interesting but not actionable. We view it but we don't learn from it. It's essential to elevate information from being merely interesting to delivering insights that induce action.
Some of the methods do this include making insights real-time, role-based and highly visual. It also helps to link the insights to recommended actions (such as guided recommendations, next best actions or Plays in a Playbook) that result is a measured user, customer or business outcome. Here are some examples.
- A sales manager dashboard shows a salesperson with a win rate equal to 45 percent. That may be interesting, but it is not insightful or actionable. Instead, present that information along with an industry benchmark that shows the average win rate is 49 percent, so that the sales manager knows the salesperson is below par and needs help.
- Include a predictive analytic that shows the revenue impact if the sales win rate is elevated to the average. Show correlations, such as that this salesperson performs an average of 6 activities per sale opportunity, while successful sale opportunities incur an average of 14 activities. Now you have an insight and can act. Facilitate action by creating links from the metric to Playbook Plays, such as a Play that shows the methods to increase sales win rates.
- Another CRM dashboard may show that marketing delivered 44 leads to sales during the month. Maybe interesting, maybe not. Instead, advise that marketing leads delivered to sales contributed 15 percent of the sales pipeline and 18% of won revenues. Then supplement the performance metric with an industry benchmark that shows the average marketing organization delivers 23 percent of the sales pipeline while Best-in-Class marketing organizations deliver 51 percent. Now you have insights, and urgency, and can choose to act.
- As a final example, perhaps a customer has a net promoter score (NPS) of 40. By itself that may be interesting but not very actionable. Showing NPS trend helps a little. But why does the score even matter? What does any particular score even mean? NPS becomes actionable when we correlate it with other factors that are proven to drive important objectives such as revenue, customer lifetime value (CLV) and customer retention.
- Compare NPS and churn history. If your customer history shows that customers with an NPS score of 20 are 2 times more likely to churn you have new learning. If the history shows that an NPS score of 70 correlates with customers that are 2 times more likely to make repeat purchases, you have a measurable upside for action. Or if history shows that an NPS score of 80 results in customers having 2 times higher CLV than customers with a score of 40, you have an actionable journey to pursue. Every company has this data. Few companies know how to use it.
A challenge in this step is that some of the most important customer metrics (i.e. CLV, customer insights, customer sentiment) are not delivered with packaged CRM software. However, the data exists and once the information use cases identify what information is most valuable, data transformation routines can be implemented to bring these metrics to the forefront.
Measure & Improve
Finally, measure analytics effectiveness and iterate. It's a journey.
Research from the CRM Benchmark Report finds that nearly 90 percent of dashboards and reports created for CRM users do not get used. Higher value analytics such as priority-based dashboards, real-time deviation reporting, predictive analytics and performance reporting with trends and benchmarks show the greatest adoption. To be successful, utilization and satisfaction must be tracked, so that unused analytics can be improved or replaced.
One More Thing
A final note. Using analytics to make faster, better business decisions is as much about company culture as it is technology adoption. To be successful, the company should start with a data driven culture that shifts its decision-making process, by discouraging intuitive, subjective and gut-based decision making and favoring fact-based and objective decisions.
Only then will the company realize better, repeatable and more profitable decision making. About 100 years ago, a smart man named William Edwards Deming provided this instruction to his team, "In God we trust, all others must bring data." That perfectly describes an analytics driven culture.
The payback from better decision making is substantial and recurring. In fact, the ability to make better and more timely decisions is one of only four sustainable competitive advantages, because it never loses value, is not easily copied by competitors and is not displaced by new technologies.