Forget Big Data, Are You Collecting the Right Data?

Sixty-three per cent of Canadian consumers expect to receive personalized offers based on their previous purchasing behaviour, personal information and preferences. And over half would like to receive a personalized offer upon entering the store on a smartphone, tablet or through an associate, according to LoyaltyOne's Canadian Retail Consumer Attitudes and Opinions Study.

There is a great opportunity for marketers to create a loyal following with a brand or company if they are able to deliver the offers, promotions and rewards customers are looking for. However, if marketers do this incorrectly, they actually threaten the relationships that customers are willing to create and maintain with companies.

The way out of this mess? Relevance. I define relevance as the effective use of customer data to outline your goals and tactics – all through the lens of improving the customer benefit. It's not cost effective to orchestrate every touch point with a customer, and that's a traditional definition of relevance we need to overcome in order to make the most out of data.

The biggest barrier to relevance — and to meeting rising customer expectations to deliver more meaningful interactions— is the customer data that companies are using to make decisions about context, message, time, and channel.

Chances are good that too little data isn't the problem. Your company is probably swimming in information. Many companies are collecting too much data in an unstructured way, so that processing the information is overwhelming.

So the question is: Are you collecting the right data?

Here are three steps to getting to "yes":

Define the business objective: Data should align with a strategy, and the best way to sync up information with needs is to articulate the outcome of your strategy: what is the objective, what is the timeline, who is the target group and what is the goal for that segment?
With each group and strategy, consider working through these points:

  • What's the fewest number of data types needed to solve for the business objectives?
  • What data is table stakes verses 'nice to have'?

Decide on a tactic and choose the correct data points: What constitutes correct or "best" data will depend on which customer group you're targeting. You need to understand the targets' current behaviours and stay focused on consumer benefit. For instance, when considering a hold strategy for best customers, the tactic could be to target them with access and recognition to make them feel more important and appreciated. To do that would require, at minimum, four data types:

  • Demographic
  • Transactional
  • Redemption mix
  • Lifestyle

On the other hand, if you're looking at a growth strategy for near-best customers, that tactic could be to target them with offers for cross-sell and upsell opportunities to increase spend. This would require two of the same data types: demographic and transactional, as well as two different ones: communication preferences and past campaign response behaviour.

Implement efficient collection and delivery: How will you collect the data and deliver insights? "Right-sizing" a data set means optimizing the data and then collecting clean material. These are done by 1) collecting the right data to support goals and tactics 2)  updating data frequently enough to support  objectives, and 3) maintaining hygiene of the dataset -- keeping what's needed to achieve goals, and storing outdated data in a data warehouse. Data sets include contact data, marketing data and transaction data. They're collected whenever a customer opens an email, comes to your website, uses the app, or pays by credit card.

If you keep customer benefit top of mind and define your objectives prior to data collection, and remember – more data isn't always better – you are on the right path to delivering relevance to your valued customers.

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Tags: Digital