Hackernoon logo7 Ways Predictive Analytics is a Boon to Retail Industry by@cabot_solutions

7 Ways Predictive Analytics is a Boon to Retail Industry

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@cabot_solutionsCabot Technology Solution

For an enterprise, it is now quite easy to get through to the target audience, make connections with them and possibly convert them. This is because of efficiently utilizing the power of predictive analytics.

Unfortunately, predicting the future is not gazing into the crystal ball and seeing what’s going to happen next, but retailers can rest assured that there is a technique akin to it in their business.

They can actually predict the behavior of their customers, know what kind of products will become successful in a particular season and plan strategies for communicating to their customers.

Data is of paramount importance when analyzing customer behavior, but to pick relevant data from the heavy influx that comes in everyday is a tedious task. This is the reason why you need to recognize a trend or a pattern and make an analysis based on it.

There is a deluge of information on the different ways in which you can make use of predictive analytics. We attempted to filter the best of them to share with you.

Let’s go through them:

1. Catching the Straying Customer and Giving Incentives

Bringing back a loyal customer who appears to be disengaging from your brand is an arduous task indeed. Especially, when there are lots of temptations that can lure them away.

Through a predictive model, you can understand which customer is straying, which customer has the potential to be a long-term user and which shopper will make his next purchase. Though this sounds almost like the crystal ball, you can understand all these facts, and more, with predictive analytics.

When you get the hint that a customer is unlikely to purchase from you again, you can try to bring him back through offers and incentives.

2. When You Need to Resonate with Your Customers

You can leverage the power of predictive analytics when you need to urge your customers to use your products or services. Like in the fashion industry, for instance. They may conduct fashion shows to exhibit their new designs and dresses and capture attention, but they still need to work harder to make it resonate with the customers and to build new connections.

Through predictive analytics and analytic solutions, it is possible for the fashion retailers to converse with the customers to known their tastes and their interests. This way, you can produce what people want.

3. By Leveraging the Power of Big Data

Everyone talks about big data and how it can be a big game changer. But when you have all the data in hand, it does not actually make sense unless you know what to do with it. Predictive analytics is the only way to make all those data look coherent.

Retailers can gain insights from learning more about what their customers need, how they shop, what they prefer to buy during each season and a lot more. They can use this information to make actionable insights and to make more deals.

4. Improved Customer Service

As predictive analytics helps to know your customer in a better way, it would help you in your marketing plan. Imagine that you are a journalist going to interview a famous director on receiving a prestigious award. You will never ask him “So which was the film you got this award for”?

Similarly, before talking to your customer, you need to know their basic interests, likes and dislikes. You can then help them make informed decisions, perform better social media marketing and answer on-site queries.

5. Aid in Setting the Price

With the data collected through predictive analytics, you will be able to set the price for your products. The information on how much a customer is ready to pay is yours. This way, you will not set the price too high or too low, but do it correctly, which is actually half the battle done.

The activity of optimizing the pricing is done by studying competitors, checking the inventory levels, studying the product’s pricing history and finally, by collating demand. When there are market changes, you should be able to capture them in real time.

6. Deciding on Shop Location

If you are planning to branch and open a new retail store, where would you open it? You can’t just choose any place at random because what will you do if you get no customers at that particular location?

Predictive analytics would tell you where your customers hang out most of the time. You can make an analytics based on the demographics, market conditions and customer’s purchasing power. When you follow the patterns represented in predictive analytics, you cannot go wrong, because you will know which area of the city they hang out more.

7. Planning Promotional Offers

It is easy to learn a lot about the lifestyle and habits of your customers by watching their behavior. They leave a trail when they make online and even offline purchases. You can collect this data, use predictive analytics to gain insights on their behavior, watch their purchasing behavior and use this to anticipate their needs.

It is important to decide what kind of promotions would be suitable to introduce because while one type of promotion is good for the seasoned customer, another kind might be more apt for a new customer. You might have to integrate attitudinal and behavioral information to learn more about your customer’s viewpoints. This is mostly done through predictive analytics.

You can increase the engagement rate of your promotional offers by making it relevant to the customers. And this is also done through predictive analytics. It would help you draw insights on “what” and “why” of consumer choices, so you can focus on personalized promotions.

Conclusion

Predictive analytics is a huge aid to the retail industry as it helps them understand and relate to their customers needs and wants. For example, Marks & Spencer utilized the potential of predictive analytics to keep their shelves stocked with only those items that were demanded by customers during Christmas.

To ensure the best of predictive analytics is procured, you need to make the best use of statistical algorithms, data mining and machine-learning techniques. By segmenting shoppers, you can send them personalized emails so you don’t spam them, and keep only the interested ones engaged.

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