If you’ve asked yourself ‘Aren’t we already doing that?’ when it comes to Big Data marketing, you’re not alone. Although marketers are feeling the pressure – both externally and internally – to evaluate and adopt new technologies and techniques, many struggle to identify how their current data-driven techniques really vary from the Big Data phenomenon at play.
- Does Big Data marketing leverage different data? More data? Better data?
- Do the technologies and processes change how I engage with my customers? Smarter? Faster?
- Are the results different? Better? Impossible to achieve with what we have today?
Well, yes and no to all of the above. It’s not about altering the data used; it’s about altering the way the data is used. And it’s not about changing how you engage with your customers; it’s about automating the delivery process to extend relevant reach. Lastly, it’s not about aiming for different results; it’s about optimizing the process to expedite those results.
Many marketers who are focused specifically on cracking the code for ‘successful mobile marketing’ are turning their sights to Big Data marketing based on the points mentioned above – data analysis, automation, and optimization. It’s just not feasible to combine manual intervention with thousands or even millions of dynamic customers and expect game changing results.
So what really makes Big Data marketing different than traditional data-driven techniques? Here are a few key areas that marketers are taking note of:
1. Leverages transactional data versus aggregated data
Due to system and resource limitations, the majority of today’s data-driven marketing efforts are being fueled by data that has been summarized at a given point in time. Customer profile classifications, segmentation allocations, risk scores, etc. are all based on consolidated information, which is then leveraged to determine appropriate marketing efforts.
The weakness of this approach – especially in the realm of mobile – is that customers continually alter behaviors, preferences and needs; therefore, a profile captured days or months prior, stands a good chance of being inaccurate when campaigns are launched. We’re all familiar with the term, ‘garbage in, garbage out’, right?
Big Data marketing technologies aim to keep the data at a transactional level – leveraging any and all available data to derive behavioral trends and patterns over time. Without this continual data stream and analysis, your marketing insights become stale and result in irrelevant – and eventually annoying – communications.
As an example, when I was heading up marketing strategy at Nordstrom, my boss purchased a baby gift on the Nordstrom.com site for her niece. Subsequent to that one transaction, she started receiving emails with offers for various baby items which insinuated she was a mother – even though she had no kids of her own and never purchased baby items before or after the single occurrence. If she hadn’t been the CMO at Nordstrom, I am sure she would have opted out of any future emails!
2. Focuses on discovery versus directed data analysis
Often times, marketers leverage data sets to search for answers versus allowing the data to derive learnings, and ultimately, make their efforts smarter. For example, you go to your business analysts and ask them to pull all customers who have decreased their usage by more than 25% over the past month for a targeted retention campaign, in which everyone receives the same message at the same time.
With Big Data marketing, the approach shifts from an outside-in to an inside-out approach. Instead of dictating which data matters, automated technologies take the lead in determining which factors contribute to churn, which customers are at risk, how their churn would impact others in their social graph, etc., and then determine the best way to act – or in some cases, not act – for each and every customer.
3. Executes against what is ahead versus “next best”
A large percentage of the data-driven technologies being leveraged today determine appropriate action based solely on historical or current events. Although this is valuable information, it is not conclusive of the action a customer is likely to take next. Instead, it’s typically an assumption based on the customer’s recent behavior.
On the other hand, Big Data marketing has the scale to analyze historical and current behaviors, as well as leverage predictive analytics – at the individual level and in comparison to other customers sharing common behavioral profiles – in order to predict future actions and impacts. This allows marketers to act on what’s best for the long haul – versus what’s best for now.
Referring back to my earlier marketing days, I used to head up Gold Card Loyalty and Retention marketing at American Express. We knew that the biggest trigger for churn was a nearing anniversary date as this is when annual fees were required – and you could typically couple this with declining usage.
At Amex, our focus was on re-selling the value of the card – discounting the value of the card by waiving the annual membership fee was not allowed. So my colleague, who headed up retention marketing for the Personal Card, and I were both tasked with decreasing the churn rates.
My colleague (aka the Personal card guy) came up with a “clever” campaign to offer $50 gift checks to get customers to stay another year – a pretty good next best offer to get an immediate save! Needless to say, their save rates went through the roof and started to crush my team’s results.
My campaign analyzed a card member’s historical spend and leveraged merchant offers that synced to their preferences, e.g., travel discounts for intrepid travelers, clothing discounts for clothes horses, etc. This approach resulted in card members using the card again and seeing the value of carrying the Centurion in their wallet.
Fast forward a year later to see how my results compared to the Personal team’s next best offer – like clockwork, the Personal card group was deluged with customers who brazenly called, not to cancel, but to get their $50 gift check AGAIN! The campaign came to an abrupt end.
And for the Gold card? We actually saw a decrease in churn and year over year call volume.
The reality is that Big Data marketing IS different and is enabling companies to engage with customers in ways never before possible – especially when paired with the mobile channel. The challenge to all marketers is to look at the data and analytics driving your campaigns today and ask yourself – can we be smarter? Faster? More relevant? There’s always room for improvement and fortunately for marketers, Big Data marketing is ready and willing.