How to use predictive analytics to transform the customer journey

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joshbrown
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It’s no secret that customer expectations have changed drastically in recent years.

Now, more than ever, customers want to be treated not as revenue-generating robots - but as people. A 2012 study by Experian Marketing Services found that 84% of customers would not do business with a company that failed to take into consideration their individual needs.

Of course, getting to know all of your customers on a personal level is a monumental - if not near-impossible - task.

Luckily, predictive analytics can help.

According to sas.com, predictive analytics is “the use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data.”

In the world of marketing, predictive analytics is used to determine optimal methods of reaching individual customers at various stages of the sales funnel to increase the probability of conversion and retention (and to reduce churn).

Essentially, predictive analytics can be used to reverse-engineer past customers’ experiences and actions taken to pinpoint the marketing strategies that resulted in a positive outcome for both your consumer and your company.

Predictive analytics is useful at every stage of the sales process. From the moment a person becomes a lead, to the time they become a paying customer (and even after they do so), you can use predictive analytics to optimise their experience with your company - and make them more likely to become loyal followers of your brand.

Predictive analytics during pre-sale stages

As you know, the customer journey begins well before an individual actually opens their wallet.

For our purposes, we’ll focus on when an individual is considered a lead, and when they’re considered a prospect.

Predictive analytics and leads

At the “lead” stage, an individual begins showing more than a passing interest in your product or service.

Of course, not every lead will become a paying customer. So how do you know which leads are worth pursuing?

Predictive analytics takes into consideration various aspects of your existing customer base to pinpoint the type of person who’s most likely to convert to a loyal customer.

According to Groove, the most relevant characteristics to take note of are:

  • Firmographic data.
  • Demographic data.
  • Geographic data.
  • Psychographic data.
  • Sistographic data.
Segmentation

By analysing your customer segmentation data, you’ll know who to focus your sales efforts on, and can work to ensure these individuals have the best experience possible while engaging with your company.

(A quick aside: predictive analytics at the lead stage are only useful when you have more leads than your company can currently handle. If it’s at all possible to provide an optimal experience to all of your leads, by all means do so.)

Predictive analytics and prospects

Once a lead begins showing an active interest in your product or service - and likely has spoken with a member of your sales team - they become a prospect.

At this stage, predictive analytics takes into consideration the segmentation data mentioned above, as well as any supplemental information your sales team uncovers during interactions with the prospect. This additional information will likely relate to the method of and frequency of communication between your team and the prospect, as well as the quality of these correspondences.

To get the most out of this data, your sales team will need to quantify “intangible” information (such as the quality of correspondence or the prospect’s comfort level). This can be done by developing a rubric or similar scoring system, allowing you to to objectively define the quality of each prospect’s experience with your company thus far.

AllBusiness.com suggests qualifying your prospects by focusing on the following aspects:

  • Their level of need for your product or service.
  • Their budget or spending threshold.
  • Their authority to go through with a purchase.

Qualifying a prospect

Keep in mind that there likely won’t be one single optimum situation with regard to these aspects of a prospect. For example, a prospect might be in dire need of your services but be hesitant to spend as much as you’re asking - but that doesn’t mean they definitely won’t go through with it. On the other hand, a more well-off customer might not need your services, but won’t have to worry much about making a purchase.

Predictive analytics allows you to see each of your leads and prospects as an individual, and do what you can do provide them with the best experience possible - knowing your efforts will make them more likely to convert.

Predictive analysis during a sale

Predictive analytics can be used to facilitate two crucial strategies that can improve the customer experience and generate increased profit: upselling and cross-selling.

Without the aid of predictive analytics, both upselling and cross-selling come across as desperate attempts to offload extra “stuff” and make more money off of the customer.

Amazon uses predictive analytics in a number of ways - most notably for creating bundles (upselling) and providing suggested purchases (cross-selling).

In other words, blanket upsells and cross-sells don’t work.

But, by using predictive analytics to strategically implement upselling and cross-selling tactics, you can make informed suggestions to individual customers that they are more likely to find value in.

Predictive analytics considers a specific buyer’s browsing and purchase history and makes an educated guess as to the value they’re looking to get out of engaging with your company. In other words, predictive analytics doesn’t just allow you to sell more products - it allows you to sell a better experience.

Amazon uses predictive analytics in a number of ways - most notably for creating bundles (upselling) and providing suggested purchases (cross-selling). Predictive analytics helps Amazon’s marketers determine other products a customer might find valuable considering their current search patterns. By providing suggested purchases right alongside items the customer is considering buying, Amazon proactively solicits extra sales (without coming across as being salesy).

 

Recommendation

While the above example is pretty standard (as in, if you just bought a new phone, you’re probably going to want some accessories, too), a lot goes on behind the scenes to make these suggestions relevant to a recent purchase. Customers who have purchased a Samsung phone on Amazon have probably purchased numerous other items not related to the phone - but those products wouldn’t show up as suggestions here.

To create these informed suggestions, predictive analytics looks at customer segmentation, trigger events, and more - then scores each area based on a set of rules defined by the company. The result is a collection of highly-specific, almost individualised suggested actions for the customer to take next.

Post-sale and predictive analytics

Predictive analytics can help ensure a company provides added value to customers even after a sale is made, in a number of ways:

  • Helping the company keep in touch with the customer.
  • Creating a strategic approach to retention.
  • Pinpointing warning signs of churn.

Using predictive analytics, a company can determine the best way to contact a customer after they’ve purchased a product. This might be through email newsletter, snail mail coupons, or even a phone call or text message to inform them of upcoming service changes.

Ideally, customers will provide you with their preferred method of communication. But if they don’t, predictive analytics can help you make an educated guess and increase the chances of your customer receiving your message.

Along with this, predictive analytics can help determine which customers are most likely to end their relationship with your business - and help you provide them with incentive to stick around. This might include providing customer service surveys, free upgrades, or discounted services - it all depends what the individual customer might find most valuable.

Even if a customer is dead-set on cutting ties, the information gathered through predictive analytics can help your company improve services for other customers before churn even becomes a concern.

Conclusion

Gone are the days of one-size-fits-all marketing. The more you treat your customers as individuals, the more respected they’ll feel - and the more likely they’ll be to engage with your business.

Thankfully, predictive analytics helps make the process of pinpointing the specific characteristics, wants, and needs of each customer you interact with - making it much easier for you to reach them at their level.

 

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