Predictive analytics involves extracting information from existing data to determine patterns and come up with more accurate predictions for trends and outcomes. It can be complicated, simply because any time you use the past to try and figure out what’s likely to happen in the future, you are taking a risk.
However, accomplishing your marketing objectives becomes harder if you don’t incorporate some form of predictive analysis. When marketers utilize predictive analysis, they become better at identifying potential clients and customers. And, once these customers are identified, targeted, and successfully closed, other products and services can then be marketed to them based on their buying habits and patterns.
Using big data, predictive analytics can be used to provide an indication of the best product to cross-sell, and the customers to sell them to. For example, if somebody purchases a designer watch for thousands of dollars, they are more likely to be an ideal target for an Audi or Porsche than a Toyota.
Intra-product cross-selling and upselling tend to be due to successful predictive analysis efforts, for example, the Dollar Shave Club is a highly successful brand that positions premium products next to the ‘dollar’ products on their website.
There are many examples of how predictive analytics happens on a day to day basis in business. However, the advancement of big data has led to predictive analytics taking on a more sophisticated role. Today, advanced algorithms have made the science of prediction far more reaching and accurate than ever before, and it’s a trend that’s certainly showing no signs of slowing down.
Here are some of the things that can be done in marketing when predictive analytics is applied to data.
Analyze and Forecast Seasonal Customer Behavior:
This is particularly true for eCommerce sites, as the most successful are those stores that highly the products that consumers are likely to want at any given time. eCommerce brands can use past data to better determine which products are going to be popular at given times of the year and use this information to target the right customers at the right time.
Target Profitable Products to the Right Customers:
Predictive analysis can be used to ensure that the right products are advertised to the customers who are going to be most likely to purchase them. For example, you’re not going to get very far if you’re displaying pop-up ads or sending emails about mortgages to teenagers. On the other hand, ensuring to target affluent customers when marketing high-end products is essential.
‘What if’ Scenarios:
Predictive analysis can help you determine what is likely to happen in a range of different scenarios. For example:
- If a product is out of stock, which alternative are customers most likely to turn to?
- Who is most likely to buy this alternative product?
- Will customers who buy an alternative product be comfortable spending more on it?
- After purchasing an alternative product due to their originally intended product being out of stock, will customers continue purchasing it?
- Which customers are more likely to continue purchasing the alternative product?
By determining what’s most likely to happen in these kinds of scenarios, brands can make better decisions regarding both which products to market in certain situations, and ensure that more sales are made by determining a priority list of items to have in stock.
Develop More Effective Marketing Strategies:
Predictive analysis allows you to study the behavior of customers in the past, in relation to previous marketing and advertising campaigns and strategies. This will allow you to not only make sure that you are targeting the right audience in the future but also ensuring that you are using messages, themes, and images that are more likely to attract them to your product or service based on past data.
You can learn even more about how predictive analysis drives marketing performance by reading this blog post from Emerson.
Learn and Employ the Best Strategies for Repeat Business:
Marketing is not only about acquiring new customers. In fact, for many businesses, it’s often more important to focus marketing efforts on existing customers in order to encourage them to return and as a result, win repeat business. While customer service plays a part, predictive analysis can be very useful in helping you determine which strategies are best used to create repeat customers. For example:
- Which marketing strategies attracted these customers to your business in the first place?
- What kind of content do they engage with the most?
- What have they purchased from you in the past and could these purchases affect their future buying behavior?
Finally, predictive analytics allows you to learn a great deal about your customers, and as a result, make them a top priority. Using predictive analytics in marketing ensures that every decision that you make regarding campaigns and strategies is made with the customer at the forefront of your mind. And, marketers need to ensure that customers are prioritized since it can lead to:
- More repeat customers
- Customer loyalty
- More brand referrals
- Positive online reviews
- Better relationships with customers
- More customer engagement
Predictive analysis is a huge factor in any online advertising today. From simple analytics such as cross-selling based on past or current online purchases to sophisticated applications that anticipate the consumer purchasing habits based on different demographics, predictive analysis is becoming the foundation on which successful online marketing advertising campaigns are built.
And as computer processing power grows stronger and data storage becomes more and more accessible, the possibilities are endless when it comes to what predictive analytics is going to be able to eventually accomplish.
in the past, marketing was simply a matter of making sure that the store had enough of the right items in stock, but this is being challenged by online shopping, where it’s much easier to get specifics about what customers are looking for at any given time, what influences purchases, what kind of customers are likely to buy a certain product, and which products are likely to be purchase together or one after the other.