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Customer Analytics Focus on What Truly Adds Value

Originally published December 17, 2009

The perceived appeal of customer analytics is the potential to garner such specific information about your customer constituency that you can accurately predict all customer behaviors and actions with enough advanced notice to identify key opportunities and take advantage of them, and maximize value to both your customers and your organization. Yet for every mature organization that has an impactful and effective customer analytics program, there are dozens, if not hundreds, of organizations that are struggling with just the mere basics.

So if you were to start from scratch, where would you start? As a technologist, my first impulse is always to jump directly to implementing a solution – customer data integration, CRM, even specific solutions such as popular online marketing and sales software-as-a-service (SaaS) solutions. This is what often happens. A typical scenario is this: a senior manager reads an article exclaiming that “analytics leads to better cross-selling and up-selling.” That manager then declares that the company must institute analytics, with the corresponding waterfall effect that the technology must be brought in so that the company can then cross-sell and up-sell (notwithstanding an absence of a plan for specifying what is being cross-sold or how that is to be done).

So as a consultant, that desire to “build” is tempered by a consistent need to focus on what truly adds value to the organization, so I lean on my four conceptual value focus areas as the starting point – decrease costs, increase revenue, maximize productivity, establish trust. The question then concentrates on what sub areas can benefit from optimization or performance improvement, what business processes are involved, and what types of customer analyses support those optimizations.

This leads into a different thought process, though, when you realize that any analysis itself is not sufficient to add value. We have to consider the holistic view of the associated business processes to understand not only how additional customer insight will add value, but what other aspects of the business need to change in order to add value. To begin, you have to ask these questions:

  1. What business process can be improved with the addition of customer insight?

  2. Do we currently have performance metrics to baseline how well the business process works?

  3. How does our business process change with the addition of customer insight?

  4. What additional resources are necessary to support those changes?

  5. How do people need to change to support those changes and what additional training is needed?

  6. How will individuals be incentivized to make those changes?

  7. How will any improvement be measured and reported?
It would be rare for individuals at any “self-aware” organization to believe that any of the business processes could not be improved, so we can assume that somewhere within the organization there is some room for improvement. The real task is finding those opportunities, understanding why it is an opportunity, determining the value proposition, and setting up a road map for figuring out what you need, how that will be deployed, and how to measure success.

To continue our example, consider cross-selling, and let’s ponder the potential answers to the question “What are the value drivers for cross-selling?”
  • Revenue – increased sales per customer leads to higher revenue.

  • Expenses – increased number of products sold at one time leads to lowered cost of goods sold.

  • Productivity – more products sold by a salesperson at one time increases volume of sales per transaction.

  • Trust – increased products per customer leads to better customer satisfaction and longer customer lifetime.
But these value drivers only make sense in the context of the sales process itself coupled with a current baseline measurement of performance metrics, such as (and these are just examples!):
  • Revenue:
    • Sales per customer
    • Revenue per customer
    • Products per transaction

  • Expenses:
    • Cost per transaction by {product, customer, location, customer profile}

  • Productivity:
    • Number of products sold by salesperson
    • Variety of products sold by salesperson
    • Revenue by salesperson

  • Trust:
    • Number of products per customer
    • Customer satisfaction
    • Customer lifetime duration
Of course, what is relevant may be dependent on the industry. For example, a measure of customer loyalty in the banking industry might be related to the number of products that customer has with the bank.

Interestingly, before we even start with customer analytics, we already have a set of performance measures. Not only that, we don’t yet have defined expectations, performance targets, or success criteria to guide where we want to start tinkering.

And that might actually be a good point to make: the objective of customer analytics (or any other data management or intelligence activity) is to maximize value along some set of measure dimensions. And when you boil it down to looking at performance measures and potential for optimization, the initial phases of determining the best set of dimensions for improvement are an engineering process. The performance measures are defined in terms of the value drivers, and the opportunities are defined in terms of the value gap. For example, if the measure of customer loyalty is defined in terms of products per customer, the next step is determining the target number of products per customer in relation to other (and more complex) performance measures such as customer lifetime value or customer profitability.

But once you have determined the targeted number of products per customer, you then have to go back to all the sales processes to examine where those processes need to be improved, how they need to change to enable the salespeople to reach those targets, how those salespeople are incentivized to achieve those goals, and how their performance is being measured and monitored. And while the collection of performance measures and analysis to determine opportunities may be expected to benefit the business, the absence of a deployment of business process improvement will prevent any benefit from being achieved. More thoughts to come in upcoming articles!

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