Data science and “big data” used to be part of the lingo of only skilled technologists. But in this quickly evolving digital landscape, the rift between marketers and businesses has rapidly closed.

Simply put, just as how inbound marketing revealed the shortcomings of traditional inbound marketing streams, businesses which choose to ignore data science run the risk of falling behind the curve. This is mainly owing to the following:

1. We have more data now than ever

A corollary of this is that while we once operated in a world of imperfect information, we now have access to a wealth of data points, which was previously unimaginable. Everything we want to know about a customer’s demographics and behavior can now be effectively tracked at all parts of the customer funnel: from acquisition and activation, to retention and conversion.

The power of data science lies in how it allows us to probe into and understand a consumer’s behavior at every step of the funnel. It allows for quantifiable metrics, so that there is no longer confusion over which stage customers drop off and become disinterested. In other words, transactions are now crystallized.

2. Personalization is not only key, but now expected by consumers

Gone are the days in which customers are unidentifiable, nebulous personas. The insights we have tapped into also mean that customers now demand personalized recommendations tailored to their specific needs. One-size-fits all product offerings no longer work.

3. Unlocking loyalty data is of increasing importance

Most brands already collect loyalty data, and understand that loyalty programs increase customer retention by providing a steady stream of income. However, many don't know what to do with the data they sit on.

Targeted personal recommendations mean that customers are now not only thought of as one-off transactions, but relationships that can and should be nurtured. By segmenting our customer base, businesses can effectively build rapport while generating multiple returns. As Schneider relates in Forbes,
“Customers will remember their experience with a brand long after they’ve forgotten a discount.”

With loyalty data, creating time-sensitive promotions can be a defining make-it-or-break it situation. One way to assess consumer desires is to release a survey; another is to give personalized, delightful rewards.

4. From retail products to e-commerce

Our case study involving Yue Hwa is exemplary of how predictive analytics can bring in multi-fold business. After all, acquiring a new customer is 5-25x more expensive than retaining existing ones. A brick-and-mortar store for authentic Chinese products, Yue Hwa already had an existing loyalty program with over 80,000 customers. The problem they faced wasn’t in implementing the loyalty program-- but rather in how to attract the right spenders for its planned event promotions like tea, arts, and culture events. In other words, how to spend the right marketing dollars.

Employing the help of a recommendation engine, Yue Hwa identified valuable customer personas to target the right segments. It could then target SMS campaigns on events and giveaways in the most cost-effective way. In numerical terms, this meant that Yue Hwa was able to achieve a 45% conversion rate.

The likelihood of generating above-average profits and marketing earnings is around twice as high for those that apply customer analytics broadly and intensively. The likelihood of achieving above-average profitability is almost 19 times higher for customer-analytics champions as for laggards (McKinsey Survey)

What does this mean for businesses?

In a market that is already so saturated, how do we identify best-in-class tools? In order to probe into deeper actionable insights, artificial intelligence is an answerable call by providing customisable, deep insights.

There is still a lot to be unearthed in the way of data science.