The marketplace has changed, that much is apparent. Customer expectations have taken a U-turn and put most previous practices to rest. People no longer tolerate being generalized and expect companies to treat them on a personal level. While this is understandable reasoning from an individual's perspective, the game completely changes when looking at this from the other side. Companies simply do not have the time or resources to get to know every single client personally. This is where predictive analytics comes in. 

The what now?

According to the very aptly named Predictive Analytics Today, predictive analytics is a branch of advanced analytics that is used to make predictions about future events. By using various methods to get to previous and current customer data, process it and extrapolate a plausible outcome.

Simply put, it helps determine what marketing strategies resulted in a sale in the past and which ones have the highest probability of succeeding in the future. It is useful in every stage of the sales process, from helping to get customers engaged to keeping them as followers of your brand. 

Pre-sale stage

It is important to know that the process begins way before any actual purchases are made. The beginning of the journey is marked when a "lead" is identified, these are merely people who've shown more than a passing interest in the product/service offered.

Predictive analytics essentially pulls up and calculates what type of “lead” is most susceptible to be converted to a loyal customer. For example, if current data suggests that university students have a bigger tendency to become customers, sales efforts can be focused on this group and the experience can be tailored to keep them coming back. 

A deeper understanding

Quite simply, the more you know, the better. Predictive analytics helps grant a deeper understanding of your clientele, and with that understanding comes stronger retention and customer loyalty. By catering to their needs and making them feel like people as opposed to numbers, customers develop a sort of bond with the brand and continue coming back for more of the same.

The best way to initiate any kind of program on this scale is to determine what kind of prediction the company needs (reduce complaints, improve sales etc.) and to determine whether there are sufficient funds to amend these issues.

This is especially useful for companies that mainly rely on retention to generate revenue. A good straightforward example would be Blizzard. Up until recently, the company completely relied on revenue generated by monthly subscriptions for playing an online game.

To improve retention, they would have to pull up all previous data and find out that, i.e. the majority of their user base is aged 14-30 and appropriately shift marketing efforts to include the bottom part of that age range into the strategy. On top of that, they could find out which content sold best and prompted the most praise, then rehash it in future expansions (Legion). The possibilities truly are endless, and more importantly – they work.

Larger data samples

Now, going with the example above, where would one get all of this data? Well, there are three main areas that companies focus on: internal sources, social media, and regulatory bodies. Internal data is pretty self-explanatory, it covers everything from customer feedback and customer support to transaction data.

Social media is probably the most powerful way to engage with clients directly and receive unfiltered feedback about every aspect of the company. Which is great since you are provided with raw feedback about every move you make.

Regulatory bodies deserve a separate segment simply because they do not really belong beside the other two since they do not provide any positive feedback. Many industries also have special governmental bodies that collect complaints and usually publicly release them after a set amount of time.

A good example for this would be the Consumer Financial Protection Bureau. They release every complaint ranging from banks and credit card companies to lenders and the like. These complaints can sting, but still provide valuable information, thereby potentially reducing future transgressions.

Data segmentation

When all of the data has been collected, it needs to be sorted. The primary basis of segmentation are: geographic, demographic, behavioral and psychographic; each focusing on a single aspect of a customer profile.

Geographic focuses on customer location and its effect on sales. 

Demographic focuses on which group an individual belongs to, i.e. age, gender, occupation etc.

Behavioral essentially categorizes customers based on the rate of usage, loyalty status and their readiness to purchase.

Finally, psychographic segmentation focuses on customer personalities, their lifestyles, and attitudes. 

 By using specific bases of segmentation, companies can single out focus groups as well as more precisely identify what kind of people their product is best suited for and, more importantly, what they’re looking to get from engaging with the company. This improves company focus, thereby improving competitiveness by knowing who to go for and hounding them relentlessly. Another big plus of segmentation is the knowledge of where to expand based on the geographic base mentioned above. 

Buyer personas

Nirmal Gyanwali CEO at Nirmal eCommerce and WordPress web design has been a strong advocate for using buyer personas in the process of developing eCommerce websites. Buyer personas are essentially semi-fictional representations of a company’s ideal buyer. It is based on all of the compiled data mentioned above and helps us internalize the type of customer we need to attract.

Think of it as an embodiment of everything desired in a customer, a template if you will. Buyer personas help put a face on the data and help bring out customer's needs, problems and how they might change.

Post-sale stage

Even though the deal has been done, there is still something predictive analytics can do. Based on previous experiences, it pinpoints the right way of keeping in touch with a customer without scaring them off or spamming them into oblivion.

Another merit is the prediction of possible churn, aka people planning to end the relationship with your business, giving you valuable time to come up with incentives to keep them around. Even if the individual still leaves, analytics can help improve services for the remaining clientele before churn ever comes into play.

 To sum up

Predictive analytics is a wonderful tool that takes a while to get going, but the information it pumps out provides a safety net for companies, big and small alike. Statistical techniques manage to do the impossible, they bring multimillion-dollar conglomerates and individual customers to the same level. By putting the days of blanket marketing behind us, customers are experiencing a more personal level of engagement, one that is sure to bring about loyalty, loyalty like we've never seen before.

Also Read: How to Boost Your Career in Big Data and Analytics
  Modified On Jan-17-2018 01:49:15 AM

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