leveraging-data-for-dollars

Leveraging Data for Dollars

 

The mantra of ‘the customer is always right’ was pioneered by department store owners like Marshall Field (founder of a successful Chicago based department store chain in the late 1800s later acquired by Macy’s). This was in a time when ‘caveat emptor’ aka ‘buyer beware’ was the societal standard for doing business.

Today, $230 billion USD is forecasted to be transacted online in 2013 as the transformation from brick and mortar (hereafter referred to as B&M) to web becomes the status quo. As an increasing proportion of retail continues to go online, it consequently generates a prolific amount of data. Most retailers pay attention to the bottom revenue line at a minimum from a data standpoint but there’s so much more.

Undoubtedly, we should still listen to our customers, but equipped with this data, I would suggest that as a company, we may actually know what they need and want better than they do themselves. Using these four steps, you can better understand your customers, meet their needs, and make your business thrive.

1. Segment

Different types of customers behave differently. In a B&M, you can actually see and interact with your customers so your sales clerk can subjectively tell you that more women between the age of 25-30 shop at your store (assuming he/she is a good judge of a woman’s age – a risky assumption, to be sure). But assuming you have 3-5 sales people across shifts, aggregating that information accurately becomes quite challenging. We wouldn’t even want to think about how to figure out who your new and returning customers are. You’d have to train your sales team in CIA level facial recognition tactics.

However, your online users are easily segmented by a variety of demographic measures. Depending on your industry and vertical you might care about some of these metrics more than others. Among some of the commonly tracked demo analytics are gender, age, income, spend habits, cart abandonment, size of household, new/returning – the list goes on.

Bring your business online, and if you take the time to invest in analytics, you’ll be able to find some pretty interesting stuff answering questions such as:

  • Are women or men more valuable customers?
  • Do certain age groups or ethnicities tend to abandon their shopping carts more than others?
  • What would free shipping do?
  • How are my marketing channels performing?

Once you start getting a hold of who your customers are, where they’re coming from and how they’re behaving you can now bucket them and analyze them.

2. Analyze

Your sales associate, Lauren, builds up a great relationship with a loyal customer, Pharrell. She knows his kid’s names, that he always comes by on Tuesdays and might even remind him not to forget his wife’s birthday. Now, imagine scanning Lauren’s brain along with all your other sales associates and aggregating all the data she has on all of these top tier, high spending, often returning customers. Analyzing your online sales, marketing and social data can yield incredible insights on who your customers are, what they’re buying, if they’re coming back, and how much they’re spending each time they visit.

Let’s say we look across all your marketing channels, demographic metrics, and conversion data and, happily, we are able conclude the following from a deep dive segmentation analysis:

  • Female customers spend more on average per transaction
  • 18-25 year old customers return items more frequently than 26-35 year olds
  • Free shipping results in lower cart abandonment rates
  • Customers coming from Facebook convert more frequently than those coming from Google AdWords

3. Experiment

Equipped with 4 analytical insights, we can start to experiment. Prior to testing, it’s critical to establish what your success metrics are. Since the retail market is on the upswing, let’s set the bar high:

  • Increase marketing dedicated towards female customers by 20%
    • expected result: 25% revenue lift
  • Redistribute all marketing efforts from 18-25 to 26-35 demo
    • expected result: 5% decrease in returned items
  • Implement free shipping across the store
    • expected result: 10% decrease in cart abandonment
  • Reallocate 20% of AdWords budget to Facebook
    • expected result: 10% increase in overall ad conversion to paying customer

Though these qualified decisions have a  typically higher probability of success than gut intuition, at the end of the day, they’re experiments so you should certainly monitor them closely. If they don’t work, revert to last best thing you were doing before.

In an ideal world, control your experiments via A/B testing (a whole other topic which we’ll touch on another time). That way you can weed out attribution of outliers or unforeseen spikes or dips in traffic, sales, or otherwise.

4. Iterate and Scale

You’re probably a rockstar and succeeded on all four fronts, boosting sales by bringing in more female customers, minimizing returns, decreasing cart abandonment, and bumping conversion rates from your online marketing channels. Since your 18-25 year old male customers are a minority, next time one of them complains or writes a nasty Tweet, know it’s not the end of the world since now you know 75% of your customers are 26-35 year old women.

Now that you have a good idea of what’s working, go back and segment further. Break down the 26-35 year old women segment and understand if they’re married, single, have kids, what their household income is, ethnicity, geographic origin, etc. Keep optimizing until you’re satisfied with your results.

Empowered with data, you will be better able to predict inventory fluctuations, seasonal variance in sales, better target loyal customers, and worry less about the customers who don’t really impact your bottom line.

If I may, in light of a our now deeper understanding of the customer via segmentation, analysis, experimentation and scale,  propose a new mantra:

“The customer is always right, until you have data that proves you can make them happier another way.”

Sources:

http://www.ecommercetimes.com/web-performance/

by

Korey Lee

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