The Hindu Business Line : Crossing the ‘i’s & dotting the ‘t’s!

How can we expect our prospective customers to give us their business and be loyal to our brand when we don’t even respect them enough to get their name right?

In a world that’s zipping into the digital age, where CRM is the buzz word and marketers are falling over themselves to build loyal customers, it’s often the small things, the details, that are the largest hurdle.

How often have we been addressed as Ms instead of Mr when being asked to take a loan or subscribe to their ‘World’s Best’ product? However, getting names right is only the tip of the iceberg.

Today, we are swamped by customer data. We have information on customer orders; from their application forms we find out about their demography and from our e-commerce sites we can understand when they used us and how they navigated. There is a huge industry that wants to sell us all sorts of information on customers and prospects.

Customer data can be a valuable resource. It can shape our business strategy, inform our product development, measure the impact of price changes and target our direct marketing and e-mail campaigns.

Too often, it does none of these things; everyone gets the same mail regardless of whether they fit the profile or not; the business measures itself on the number of customers, overall churn and average value.

Before we explore how to profile and segment we should ask the question, why is this rich data untapped? In my opinion, the reason is simple. The data is explored, but only to inform on the basics of a campaign. The knowledge is seen as marketing knowledge, not business information that can not only empower direct marketing activity, but influence greatly the overall success of a business by shaping its total strategy.

After all, a business’ number one objective is to make money. Income comes from customers and so do substantial costs such as costs of customer service and delivery logistics. We ignore customers and the variations between them at our peril.

In their need to be the first off the block, some marketers often leapfrog over the essentials of customer management — optimally leveraging the database. Especially when an amazingly small number of people can make or break your brand. It is, therefore, important to segment the customers and know which ones contribute more than the other so that we can deploy our marketing budget more efficiently, spending on those customers who matter versus treating all customers big and small equally.

Another philosophy that marketers and agencies embrace is, ‘the more people I reach, the more I’ll enrol’ and hence the more profitable my venture will be.

In direct marketing, it’s not as important to reach a multitude of people; what is far more important is to reach the right kind of people, the people who count.

The key to reaching the right kind of people lies in profiling your target audience, identifying these customers, understanding who they are, where they come from and most importantly what makes them loyal to the brand. This exercise is not as onerous as one might imagine ... it simply involves talking to some people who are incredibly loyal to our brand and understanding them better.

The benefit of this exercise is two-fold. Firstly, it helps us understand a loyal customer better and thereby makes it easier to look for and recruit more such customers. And more importantly, once we’ve really understood our loyal customer and what his likes and dislikes are we can actually start servicing him better and make him feel more special. The natural fallout is that he’ll love our brand even more and thereby increase his interaction with it, thus increasing the bottom line.

Today the world has moved beyond the realm of just customer management into Customer Ownership, which can be defined as getting the brand to say, this is MY customer, and getting the customer to say this is MY brand.

Every day we hear about the average customer. In today’s market the concept of the ‘average’ customer is a dangerous misnomer.

“We have a million customers; each is worth Rs 1,000 per year to us; we lose 10 per cent per year.” Averages are very dangerous things. Let me give a simple example.

“We have a million customers; on average, each is worth Rs 1,000 per year to us; we can afford to invest Rs 200 per customer to recruit new customers.” Does the following statement ring true?

On the face of it a sound business strategy, but wrong! Let me make the same statement again, but with a bit of customer insight added.

“We have a million customers; on average, each is worth Rs 1,000 per year to us; if we invest Rs 200 per customer to recruit new customers, then 85 per cent of them will be unprofitable.”

Would you as an MD buy this strategy? I doubt it. Both statements are correct. The key message is simple. Don’t take the top line answer at face value; by summarising the behaviour of 1000’s of customers into a couple of numbers one hides the truth.

Customers are different!

For example, what do we mean when we say that an average clothing customer spends Rs 1,500? Customers may spend anything from Rs 500 to Rs 20,000, so how can we use the figure of Rs 1,500 to predict what another customer (the next customer) will spend?

Is the ‘average’ customer buying only for him/herself? What makes some customers kit out the whole family and thus reach a higher spend? Is it wise to even think in terms of an ‘average’ customer spending only Rs 1,500? Is this not in danger of becoming a ‘self-fulfilling prophecy’?

Perhaps we have two different types of customer, some spending only Rs 500 and others who spend Rs 15,000. Should we not treat them differently? But we can’t treat them differently until we recognise that such differences exist – all perhaps buried within our data ‘average’.

We have to get underneath the skin and start to profile and segment our customers to see what our data is really telling us. If we do, it will make a huge difference to our business performance.

What data is trying to tell us

What are the characteristics (profile) of our customers? What makes them similar to or different from the population as a whole and the market in which we trade?

Is this profile changing over time? Do different media attract different types of customer?

Do some customers appear to prefer different sales channels?

How can this knowledge improve our advertising, direct mail and e-mail? How can direct or above-the-line be tailored to optimise what we know about our customers?

Does this help or hinder our ability to cross-sell or up-sell to them?

Do we define different customer segments? Will these segments alter what we promote? When we promote it? Which sales channels we use? Are there possible partnerships other organisations?

Which customers have reached each stage on the loyalty ladder? Which have the highest propensity of lapsing or defecting? What are the characteristics that lead to lapsing? Can we avoid recruiting potential defectors, or can we change their behaviour before they lapse?

Which customers are ready for their next purchase? And what should we offer them? Can we break down next year’s business objectives across each segment of our customers? What are the individual goals for each segment and what do we need to do to achieve these goals?

All these questions can be answered by the data we mine. In order to answer these questions we need to know our customers.

How profiling works

The first stage in any profiling exercise is the data audit. This is a review of what is available and an analysis of the quality and the source of this data.

Profiling takes two basic forms:

1. Comparing characteristics of customers within the data set. For example, what is the profile of buyers of product A versus product B or how is the profile of customers changing over each year of acquisition. This step does not demand any extra data from outside the business.

2. Comparing characteristics to a ‘national’ population. Are the customers buying our product different to the profile of all buyers of this type of service? This profiling needs us to source data from outside of our business with which to compare our profile to the bigger picture. If your business is solely trading in Mumbai, it will have a different profile of customers, not because they are different to the national picture as such, but because of where your stores are located.

Searching the data for flaws

Both forms of profiling use similar techniques: first we have to understand the key characteristics of our customers, then we compare them with either sub-groups of our own data or with the target market or population as a whole.

There is another major benefit of profiling: it helps us to really understand our customer data in ways that otherwise we might not appreciate. Here, it is important to remember that most businesses collect their data as part of processing a transaction. This might be account or order-processing or another operational system. In other words, the data often isn’t collected primarily for marketing purposes and as such may have serious shortcomings.

For example, there may be a world of difference between the record layouts in our computer systems and the data we have actually captured. We may find data missing from such fields as date of birth or gender because it saved the operator time to omit it, especially if it was not essential to the transaction.

Before we can properly understand our data, and use it for profiling, we must be aware of any constraints or deficiencies in its original collection and processing. It is therefore imperative to validate the data before embarking on a data driven marketing exercise.

A final word of caution, when we do go in for data-driven marketing. We must remember to cross the ‘i’s and dot the ‘t’s ...

You know what I mean ...

(The writer is President, OgilvyOne Worldwide, India)

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