Customer retention requires data-driven predictions

To scale revenue and drive profitability, companies need to shift their focus to improving retention rates and increasing the average lifetime value of their customers.

Customer retention requires data-driven predictions

Customer acquisition is a large focus for nearly every brand, and while this is important to driving growth (especially for early-stage companies), there comes a point when return on ad spend plateaus. To scale revenue and drive profitability, companies need to shift their focus to improving retention rates and increasing the average lifetime value of their customers.

After all, acquiring a new customer can cost five times more than retaining an existing customer. On top of that, approximately 80% of your future revenue will come from only 20% of your existing customers — so how can you find more of that 20% and further engage them?

Focus ad spend on high-LTV audiences

An effective retention strategy is inextricably linked to a strong acquisition strategy. If you can acquire customers from the get-go who are highly likely to come back repeatedly, you’re extending the reach of your marketing dollars far beyond that first purchase.

One way to identify leads and prospects who are likely to become high-LTV, loyal customers is to analyze what your existing loyal customers look like. Perhaps loyalty hinges on how often someone purchases or how much they spend; but if you dig a little deeper leveraging third-party data with your proprietary data sets, you might uncover even more telling details that serve as indicators of LTV.

From there, you can generate and target high-LTV lookalike audiences to increase the likelihood that those new customers are on track to become your next loyal customers.

Get ahead of churn-prone customers

Churn is a challenge for many brands that rely on repeat purchases to drive revenue. Luckily, the democratization of machine learning has made it easier to predict churn.

In fact, improving your retention rate by just 2% is the equivalent of cutting costs by 10%. This is because retaining existing customers is much more cost-effective than spending time and money on strategies to acquire new customers or to reactivate lost ones. So if you can accurately predict and then prevent churn by getting in front of risky customers before you lose their business, you'll be doing yourself a long-term financial favor.

When it comes to making accurate predictions about which of your customers is churn-prone, rich and informative data is key. Faraday uses machine learning models to find patterns and similarities between those customer sets in order to identify churn-prone customers. As your customer base grows, reiterating these analyses can prove helpful, as you might see shifts in behaviors and overall profiles as time goes on.

But knowing who is likely to churn is useless if you don't have a plan to act on that information. Those predictions need to be easily accessible in your ESP to segment your risky customers and put engaging promotions in front of them.


Churn modeling case study

Invest in personalized engagement tactics

Understanding and catering to your churn-prone customers is a financial investment, but investing the time and money into analyzing and personalizing your methods of engagement — and improving them by even 5% — can increase your company's profitability upwards of 25% and save you money in the process.

But it's not just the risky customers you should be concerned with when it comes to personalizing marketing creative. 89% of companies believe that customer experience is a key factor in driving loyalty and retention — giving your other prospects and buyers the same amount of attention helps to build trust and loyalty from the very start.

Personas are a great place to start when you're beginning to personalize your creative for various audience segments and customer groups. They can be high-level and just based on transactional history, or you can flesh them out with additional consumer data to create richer profiles to work from. Either way, providing more relevance in your communications with your customers shows your target audiences that you're putting effort into aligning with their interests, values, and motivations — ultimately building trust.

The loyalty that results from personalized customer experiences directly translates to the probability of a customer referring a friend to your brand. Neilson reports that 92% of consumers trust word-of-mouth referrals over all other types of advertisements, making loyalty programs and referral perks an incredibly valuable marketing opportunity (and a tactic many successful brands rely on).



Learn how Faraday helps brands maximize customer value by putting data science to work.