The message, “Helping will sell, selling won’t help,” is something that we repeat often here at TSIA, through our books, conferences, and research. That is because it is a concept that we truly believe in, because we’ve seen it in action time and again. If we apply this thought to data analytics, would it imply that data insights for the customer will sell, but data insights for the salesperson won’t help? I suppose it depends on the type of insights you’re able to gather.
Explaining How LAER Applies to Data Analytics
Before we unpack this riddle, let’s provide context with TSIA’s LAER framework:
- Land: Land a new customer
- Adopt: Optimize the customer’s adoption of your platform
- Expand: Expand your customer’s footprint via upsell and cross-sell
- Renew: Increase renewal rates of your customers
From a data perspective, there is a considerable overlap across all of the phases of LAER.
In order to LAND customers, you need to match customer demographics, financial status, and customer desired outcomes with your product’s positioning and portfolio. For each potential customer, you should estimate their need or propensity to purchase each of your offerings based on the priority of their outcomes, and your offerings’ abilities to help them achieve those outcomes.
After the new customer is on board, in order to optimize the ADOPT phase, you’ll need to track how well the customer is leveraging the purchased offerings. We refer to this as “consumption analytics,” i.e. understanding how customers are consuming your products. Tracking an overall customer account health score, as well as adoption scores of each purchased offering, will enable you to proactively improve adoption. You’ll then be able to identify which customers of a certain profile and tenure are under-utilizing or getting the most of your platform.
For the EXPAND phase, all of the previous data features are in applicable. One important note is that you’ll need to consistently refresh your customer’s propensity to purchase each of your remaining offerings.
Lastly, for the RENEW phase, the relevant data is the aggregation of all the previous steps.
Successful Collection of Data Insights Depends on Operational Alignment
So, if the data is the same, then how can the statement, “Data insights for the customer will sell, data insights for the salesperson won’t help,” be true? It all comes down to how you and your entire company leverages and delivers the insights to your customer. The data model is easy, in fact, it can be as simple as the below mock-up of a customer insights table.
The hard part is organizational alignment and optimizing multi-channel communications with your customer. If your Marketing, Sales, Customer Success, and Support Services teams all have the best insights and a unified view of the customer, things can still go horribly wrong if the teams continue to operate in silos. The customer can be overwhelmed by conflicting messages around the same topic from different departments, or, the customer could receive no communication because of internal miscommunication of which department is responsible for handling their issue.
If your company is devoted to alignment and breaking down operational silos, how can analytics help? Reports and business intelligence (BI) can be useful and are a good first step, but they require your employees and customers to pursue the insight. The next value add of analytics should focus on triggered events and automation. If a proactive “adoption play” on purchased offering #2 is needed because your customer’s three-month adoption score of that offering is in the bottom quartile and the customer’s overall health is red, automate the creation of a task for your customer success manager (CSM). Further, via automation, communicate and coordinate with other departments that this is priority number one, and provide clear line of sight to the status and timing of the event.
Lastly, to get the most out this data driven automation, your analytics team needs to provide predictive and prescriptive insights to your team, as well as your customer. For each suggested play, whether it’s adoption, expansion, or renewal, analytics should provide an estimated impact on the corresponding KPI (predictive) and guidance on the nature of the play, such as scripts (prescriptive). Once this automation with both predictive and prescriptive insights are in place, your team can then optimize the impact of each play.
Learn Even More About How Everyone in Your Organization Can Better Use Data Analytics
So, the data underlying helping and selling are the same. Does your company have this data unified in one place? Are you organizationally aligned to agree on what to do with the data, and then, are you communicating internally so as to optimize multi-channel communications with your customers? Are you getting high value-add insights and data driven automation from your Analytics team? Regardless of how you answer these questions, TSIA is here to help you optimize your technology service and sales motions.
I’ll be presenting a couple of sessions that apply the concepts presented in this blog into real-world, actionable plans at our upcoming TSW conference, taking place May 7-9 in San Diego. The conference theme is “Blending Service and Sales Motions,” where you’ll join other leaders in tech to learn about how to create a better environment of collaboration for the good of your company and for the good of your customer. I hope you’ll join us! There’s still time to register, and we offer group discounts if you’d like to bring your team.
In the meantime, be sure to subscribe to the blog, as the rest of the TSIA research team is going to be sharing how this theme relates to their specific area of research up until the conference to show that cross-functional collaboration impacts everyone.
Read more in the "Blending Service and Sales Motions" series:
About the Author
Jeremy DalleTezze, Ph.D., is the senior director of data analytics research for TSIA. He works directly with members to identify and study the emerging trends in consumption analytics. He also is the head of TSIA’s internal analytics team, which provides analytics support for TSIA, including the research team, by analyzing their benchmark and survey data. Jeremy aims to help TSIA members understand the more advanced aspects of the analytics world so they can improve their businesses.