“What Exactly Is Customer Centric Analytics?”

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I was having a conversation with a potential customer recently. After we had finished our obligatory introductions they began our chat with the question, “So, what exactly do you mean by ‘Customer Centric’ analytics?” 

This is a question I get asked quite a bit. In fact, explicitly using the term “Customer Centric” in Zeenk’s external brand positioning and product description encourages it. In response, I turned around and asked them: “What do you think ‘Customer Centric’ means?”

On this specific occasion, my prospective customer drew from some familiar customer adages in their response, e.g. “Customer centricity means to put the customer at the core of your business and to ensure every customer is delighted with their experience, and of course the customer is always right.” Which in and of itself is not a bad mindset to have. Afterall, anyone who buys your product or service has some value to you. But from an execution standpoint, it is an incomplete strategy that is not the best for the business.

A more accurate description of a Customer Centric approach would be to say that “It is an approach that puts your most valuable customers at the core of your business.” Or to use the more formal definition penned by Peter Fader, the author of several books on the subject of customer centricity and a professor of marketing at the Wharton School of Business, “Customer Centricity is a strategy that aligns the development and delivery of a company’s products and services with the current and future needs of its highest valued customers’ long term financial value to the firm.”

To borrow more from some of Mr. Fader’s thought leadership and adding my own experience with customers, I would summarize the customer centric approach in three fundamental premises: 1) each customer has a different value 2) executing against customer value is better for your business, 3) operating in a way that manages against the heterogeneity of customers will yield better results.

Customers have different value

Each customer has a different value to your business. Your best customers will stay with you longer, purchase several products over time, recommend your products to others, etc. Some of your less valuable customers will make only a few purchases and then disappear. The variation in these values is often measured by a customer’s lifetime value (CLV).

Customer lifetime value has been defined in several ways in the business literature. At Zeenk, to keep it relatively simple we define customer lifetime value as the amount of net revenue you can expect to generate from a single customer over a period of time. The net revenue is the cash you receive on the order less the cost of the goods you sold, less other costs you may incur in providing the product, and finally reduced for the return rate. 

Let’s look at a simple example where a customer buys $100 worth of goods in a single order and the company charges a flat fee for shipping but incurs a variable cost for shipping the product.

So this customer is expected to generate on this single order $44.50 in cash. That cash has to pay for the cost of acquisition for that order and leave you some operating cash to pay for your expenses.

So now suppose one customer will buy once and leave. That customer is only worth the above.

Suppose another customer if they buy, may buy again 8 times out of 10 in the next three months.  A little math shows that this customer is likely worth $131 over the next 12 months and will likely be worth even more over a long period of time.

This illustrates why you want more of these customers and want to align your marketing, product choices, and operations to acquire, retain and service your highest valued customers. This is not to suggest that you should completely ignore or fire a segment of your customers, but your investment in them should be more commensurate with their value.

To give some real life examples, we were recently speaking with a larger retailer that has a direct business, a retail business, and a franchise business. The marketing team manages to an average cost across the direct e-commerce business.  However, the profitability of every customer depends not only on what they buy, how they were acquired, but also on which zipcode they live in because the retailer has to pay fees into their franchisees depending on the zipcode. Optimizing against this requires configurable software, but the differences in customer value even at first acquisition can be considerable.

Another example, we were speaking with a small DTC company that was working on optimizing their product mix and availability of that mix by location.  The complexity for them was that they had a single fulfillment center in California, but the product is heavy and so shipping costs depend critically on location.  So this company needed to segment out who was most likely to purchase multiple times and take into account the location of that purchaser in order to determine the potential CLV of the customer.

Image depicting 3 separate customers all acquired at same CPA but CLV value assigned to any

Executing against customer value

Your business’ capital, people, attention, technology, etc. are finite. Business leaders have to decide how to most effectively allocate their resources across product, marketing, customer service and retention to best grow their revenue and profit. Unfortunately, many marketing executives will often measure the “effectiveness” of their programs based on average cost metrics like CPA, Customer Acquisition Cost (CAC), etc. and allocate their budget accordingly. For most businesses, this is not profit maximizing because it does not factor in the value of the customer that is acquired which can lead to lost profits.

Let’s unpack CPA and CAC a little bit to see how they are different and require some care to measure correctly. Cost per acquisition (CPA) is a metric that is broadly reported by analytics platforms like Zeenk, and is computed on all the major advertising platforms if you provide them a conversion event.  The CPA is the amount  spent in a period on some subset of your advertising channels divided by the number of orders that have occurred where the users who placed those orders were tracked to have been exposed to that advertising.

Average CAC is straightforward and you don’t need anything except Excel to compute it.  To get the average CAC for a month just pull the orders for a month and figure out how many unique customers placed those orders.  Remove any customer who ordered from you before.  Then download your total advertising spend for the month and remove the cost of any campaigns that were used to message or retain already existing customers.  Divide these two numbers and you have an approximate average CAC.  However, to go deeper and really look at a single customer’s CAC you need a more sophisticated analysis.

While CPA and CAC metrics are useful because they give marketers an idea of how efficient their media spend is, neither provides insight into the quality of the customers that have been acquired or the long term value each will have to their business.

To further illustrate, let’s refer back to my previous example where one customer made 1 purchase over a 12 month period and was worth $44.50 in net cash compared to another customer that purchased 8 times over that period and was worth $131. Imagine both customers were acquired via a Meta campaign that calculated that each customer was acquired at the same CPA. In the eyes of the organization these customers are considered equal and should be treated as such.  When in fact this is not the case. The $131 customer has higher value to the organization and warrants greater examination by marketing to determine what key attributes could be leveraged to optimize their targeting, messaging, etc. in the channel.

Maintain the heterogeneity of customers

Creating averages across your customer base are easy to compute and should be computed to run the overall business.  But companies have to dig deeper to find opportunities, focus their resources and grow the business. Maintaining the heterogeneity of individual customers when developing CLV and CAC models can help you more accurately identify your best customers. Further, it also enables you to identify key characteristics of the different customer groups so you can create more targeted strategies that will generate better results.

It turns out that for most businesses, the distribution of CAC and LTV can be very wide. Often at least an order of  magnitude.  And more importantly it is not well aligned with the value of the customer. For example, a Zeenk customer has a distribution of $10 to $200 in CAC and a similar distribution in CLV, but for some customers they are paying $20 to get a $200 customer which is great but for others they are paying $200 to get a $20 customer which is very bad.

Visual depiction of Zeenk's Customer Lifetime Value (CLV) vs. Customer Acquisition Cost (CAC) ratio distribution chart

“So what do you mean by ‘Customer Centric’ analytics?”

Back to my prospect’s (who is now a customer) original question –  “So, what exactly do you mean by ‘Customer Centric’ analytics?” Customer centric analytics is a data reporting and analytics solution that enables businesses to measure and optimize the performance of their business against the value of their customers. From a marketing perspective, it enables you to optimize your channels and creative so you retain and grow your best customers, and acquire more customers of higher value.  From a product perspective it helps to select the best product mix for each customer and across customers.  From an operations perspective, it helps to think about how to ship, service, and support your customers.

What does a “Customer Centric Analytics Solution” include?

It’s an analytics solution that should empower businesses to execute a Customer Centric approach to optimize the performance of its business. This is supported by several key features:

  • Accurate measurement and forecasting of customer-level CLV at scale
  • Ability to segment customers based on CLV, CLV:CAC ratios, CLV vs Churn Probability
  • Provide reports that identify key attributes of customers and customer groups that impact value and retention
  • Ability to activate these insights in their marketing channels to improve quality if acquired customers and retain their highest valued customers.

In short, a Customer Centric Analytics solution is Zeenk.