At Sprocket, we pride ourselves as being the group of nerds….ahem….professionals who can help you grow value from your existing customers. As such, customer lifetime value (LTV) becomes a very important metric to track in order to determine whether your customer experience efforts are paying off.
Traditionally, LTV focuses on the value of a customer from the perspective of revenue or profit. How much revenue are we going to realize over the entirety of our relationship with a particular customer?
An important metric, but I would argue, an incomplete one.
Many companies have an incomplete view of LTV, and there’s a key source of value that is frequently omitted from the LTV equation: DATA.
Show me the DATAAAAAA!
Forbes recently reported that we produce over 2.5 quintillion bytes of data each day, and the pace is exponentially increasing. Let’s just agree that none of us can really comprehend how large 2.5 quintillion is, so for argument’s sake, let’s just say it’s a lot.
However, you don’t need quintillions of bytes of data to begin using data to drive increased revenue in your business. Many companies overlook the incredible power of some very common sources of customer data:
- Point-of-sale systems
- Loyalty program platforms
- Voice of customer surveys
Data: The Gift that Keeps on Giving…Value!
When a person becomes your customer, they begin feeding your data sources with their information. You learn demographics about them, you see what they purchase, when, and for how much. You see whether they engage with programs and promotions you put in front of them and which ones are particularly enticing. And, you hear from them directly when they fill out voice of customer surveys or call into customer care.
Traditional definitions of LTV would see a short-term customer as objectively less valuable than a longer-term customer. And, the “value” each customer creates for the company would only be captured once per transaction.
Sure, the longer a customer is with you, the more data they tend to generate. However, even customers who fall into the “one and done” category or those who have a relatively short lifecycle with you generate data that is incredibly valuable. Simply knowing the types of customers who are short-term customers is powerful!
When we expand our view of LTV to include the value that the customers’ data provides, the value of both of these fictitious customers drastically increases. The data from both types of customers helps us understand and predict the likely behavior of future customers.
Let’s dive a bit deeper.
Don’t Leave [Data] Value on the Table
We’ve all heard the saying, ‘Don’t leave money on the table.’ Meaning, don’t miss out on capturing revenue any chance you get. However, I urge you to think more broadly and to not leave [data] value on the table.
If you have data sources such as the ones I listed earlier, you are sitting on a gold-mine of value that can help you make more intelligent decisions, more engaging customer experiences, and offers that resonate better with your customers.
Here are just a few things you can be doing with your customer data to drive additional value:
- Predicting whales & minnows – When you acquire a new customer, are they likely to be a high economic value customer (i.e., a whale) or a lower economic value customer (i.e., a minnow)? Predictive modeling using your existing customer data can help you determine exactly that. And, knowing the difference early on can help you determine how much time and effort to put toward nurturing that relationship. The smarter you are with how you allocate your sales and support teams’ time, the more efficiently your internal ops teams can run.
- Tailored offers – What type of offer is most likely to drive generative sales and revenue from your existing customers? Is that offer likely to convert similarly for all of your customers, or will it work better for certain segments? How can you optimize the offers you send to your customers to yield the largest increase in revenue? Unsupervised machine learning to generate meaningful customer segments and predictive modeling to optimize your offer strategy will help you generate more revenue from your existing customers while helping them feel like the offers you’re presenting to them are relevant to their needs and desires.
- ‘Personalized’ communication – Undoubtedly, you have your own brand identity. Internally, you know “who” you are as a company and how you want your brand to look and feel to consumers. However, the degree to which that internal brand identity matches the identity your customer segments resonate with will vary based on consumers’ differing values, desires, needs / jobs to be done. Using unsupervised machine learning techniques, it’s possible to find meaningful groups of consumers who are similar to one another in terms of these attributes. Furthermore, a smart and creative application of predictive modeling makes it possible to apply these segments to individual consumers in your database. This makes it possible to tailor and personalize communication to consumers, based on the unique way they connect to your brand. Now that is extremely powerful.
Capture the Missing Link
In this post, I have merely scratched the surface of the value that your customer data can provide. Not only can it help you drive more revenue from your existing customers, but you can reduce spend in areas like advertising and communication by putting the right message in front of the right person at the right time.
It’s time to expand our thinking around customer LTV. It’s time to start thinking about the massive value that customer data can provide for your company, and putting that data to use to capture that value!
Curious about how this might look for your company? Reach out! We’d love to show you what your data can do.