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Today, marketers are relying on analytics to aid and guide practically every decision they make. Implementing marketing analytics allows organizations to pinpoint specific groups of customers, measure success, and drive decisions.
This is more crucial than ever. According to Salesforce’s latest State of Marketing Report, marketers’ average number of data sources has grown 20 percent since 2017. Though organizations are taking great strides toward incorporating more data tools, there are still many opportunities for how to capitalize on all that analytics can offer.
These three considerations can help guide brands as they aim to enhance the customer experience through analytics.
Many organizations used to be guilty of sending one-size-fits-all communications to their entire customer list. This was an issue for multiple reasons. For starters, those email lists contained inactive customers, causing organizations to spend more money than necessary. It was also an issue because of the lack of customization. Sending the same email to every customer does not make anyone feel special—or valued. And if customers don’t see anything in the email that appeals to their life specifically, it’s likely that you’ve lost that customer.
Many organizations have moved to more targeted messages through the use of predictive analytics, whether that encompasses the kinds of products the customer has purchased in the past or products based on their search history. While this approach certainly helps to remove those inactive customers, many companies are still needlessly spending marketing dollars on customers who do not need an offer or incentive to drive a purchase.
In some cases, the goal of the offer is to reward, surprise and delight, or incent longer term behavior beyond the initial purchase. However, for those companies trying to maximize their discount budget and generate the most ROI for that offer, removing customers that will purchase regardless is an imperative.
A single model can identify customers who are likely to respond to an offer or make a purchase, but it doesn’t tell you which of those customers needed the offer in order to make a purchase.
This is where more advanced analytic methodologies, like ‘Net-Lift’ or ‘Uplift’ modeling can help.
Uplift modeling is actually best executed through the use of an initial test, where the target group is randomly broken into two parts: one group gets the offer and the other group gets nothing. After this initial test is completed, uplift modeling analyzes the data and identifies which of those customers only purchased because of the offer, and which customers did not need the incentive to make a purchase. This not only makes your marketing efforts and campaigns more efficient, it also allows you to see a higher return on investment while enabling you to take these learning and refine further for future campaigns.
It’s also important to note that you don’t want this group to be selected using an existing model because it will bias the selection of customers toward those you know are already likely to be a good customer.
Uplift modeling enables companies to maximize their marketing spend—and the ROI of campaigns—by targeting offers to only those customers that need it. There will always be customers who are extremely likely to respond, and customers who are extremely unlikely to respond. Where we identify and focus on the ‘moveable middle’, we can build meaningful loyalty experiences.
Of course, as analytics continue to play an increasingly significant role in the customer experience, more advanced techniques will allow for enhanced personalization, more impactful campaigns, and maximized marketing spend. But as data and analytics capabilities continue to evolve, they must always revolve around the customer experience, creating meaningful interactions that foster long term loyalty and advocacy.
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