Predictive analytics has long been used in a business to predict future events using data, statistical models, and machine learning. Accurate forecasts of future events can help any company stay ahead of the competition by anticipating consumer needs ahead of time. Altering your product or service or your marketing and advertising based on predictive analytics can set you apart on the market and help you beat your competitors.
Predictive analytics is not without limitations. Black swan events are capable of defying even the most advanced learning algorithms. However, data analytics remain a powerful means to make educated guesses about consumer behavior. When applied properly, data-driven marketing can help your company thrive in a competitive market.
Predictive Analytics: Collecting And Analyzing Data
Today, there are more options than ever before to collect customer data. The volume of data at your company’s disposal can help you glean valuable insights on consumer behavior, values, interests, and more. This data can not only potentially predict future events; it can help you figure out the best means to market to your audience.
Different types of data can give you different information. Google analytics and social media often give you insight on the content your customers respond to best. This allows you to improve ad campaigns and website design to better customer experience. Your own brand sales figures can help you create sales forecasts and advertising campaign data to optimize your conversion rate.
For predictive analytics specifically, there are roughly three different measurement models you can use:
- Cluster Models: These algorithms segment audiences based on factors such as past purchases, demographic data, and brand engagement.
- Propensity Models: These data sets evaluate and predict a customer’s likelihood of taking a particular action.
- Recommendations Filtering: By evaluating past purchase history, recommendations filtering identifies future sales opportunities.
When combined, all three measurement models can help you create guidelines for marketing campaigns. They answer myriad questions about how consumers behave and make purchasing decisions. Predictive analytics answers three basic questions:
- Who should you be targeting? You can pinpoint your most loyal customers and most important demographics through predictive analytics.
- What should you use to get your audience’s attention? You can learn a lot from analyzing data such as what type of language and media most appeals to your audience.
- Which channels will best communicate your message? Looking at hard data, you can see whether your audience is more likely to open emails, click on Instagram ads, and so on.
How Can I Use Predictive Analytics In Marketing?
Predictive data like this can then be used in prescriptive analytics. This helps you determine how to use the data collected in future marketing decisions.
Better Understand Consumer Behavior
Marketers frequently segment audiences based on known interests, demographic information, and more. Predictive analytics – particularly cluster models – allow you to clearly see how your consumers behave at different points in time. Essentially, you can track your consumer’s journey from their initial interest to their final purchase.
You can use this information to create targeted messages for both specific audiences and specific points in the sale process.
For example, female customers between the ages of 20 to 23 most frequently browse a particular type of running shoe on your website. They tend to be online between 2PM and 4PM each day, and almost always access your site through a mobile device.
You could employ a retargeting campaign for users who have already clicked on your ads. Create ads for those specific shoes and run those ads on popular mobile apps such as Instagram in the afternoon. This could potentially increase conversions.
Marketers often condense data points into “scores.” For example, you may have a likelihood score for each buyer rated on a scale of one to 10. The higher the score, the more likely this customer is to engage with your business and make a purchase. Companies often have “Loyalty Scores” as well, which determine which customers are most likely to become lifelong customers rather than making a single purchase.
All of this helps you figure out how to most efficiently allocate your marketing budget.
For example, say you are a company that sells camping and hiking equipment. You find customers living in the Pacific Northwest between the ages of 34 and 43 are most likely to become long-term customers. Customers between the ages of 23 and 28 living in the same area have a high buyer score. However, they have low loyalty scores.
It would be wise to advertise to both demographics – they’re both generating revenue. However, targeted, personalized messages and advertising are more likely to entice the 34 to 43 demographic as they are the most likely to show loyalty to your brand. Therefore, you might spend more money on marketing campaigns targeted toward these long-term customers as opposed to one-and-done buyers.
Predictive analysis can not only help you decide which demographics to target. It can help you identify the proper channels and times of year purchases to run marketing campaigns. Camping and outdoor gear likely sells best in early spring, for example, so you could plan to put more money into marketing during that time period.
Marketing automation is becoming an increasingly popular means of efficiently targeting customers. Artificial intelligence and machine learning software make predictions about consumer behavior based on precise mathematical data analysis. Smart analytics predict and detect trends, identify weak spots, and find ways to optimize marketing campaigns.
What are the best means of marketing automation?
Chatbots on social media channels and your company’s website can answer common questions and queries 24/7. They also provide your company with invaluable insights about user experience, behavior, and weak spots within your company.
Content intelligence software and AI can provide you with data-driven feedback and insights about consumer behavior. You can use this information to tailor marketing strategies and campaigns to a specific audience.
Dynamic pricing can help you increase sales and conversions. This is a pricing strategy in which you set flexible prices for products based on current market demands and customer behavior. Website bots can monitor user behavior such as browsing and search history and provide users with real-time pricing based on their preferences and your current supplies.
Anticipate Customer Needs
Predictive analytics helps you anticipate customer behavior from the beginning to end of their shopping journey. This can help you create tailored messages to attract initial customers, but the potential goes well beyond this. Predictive analytics allows you to create messages that will retain customers in the long term, which is better for both your reputation and revenue stream.
The amount of information available today is astronomical. Information gleaned for predictive analytics can help you determine customer shopping journeys broken down by factors like gender, location, region, and more. This can help with everything from your initial point of contact with a buyer to retargeting campaigns.
For example, say women living on the East Coast frequently visit your website and look at a particular lamp. While they spend a lot of time on the page – and sometimes even add the lamp to their cart – they rarely make a final purchase. You could create a retargeting campaign for this product or similar products based on predictive analytics.
What might be stopping these women from finalizing their purchase? The information you have about this subset of customers can help you figure that out. Maybe they respond to a particular type of wording. Maybe the price is a little too high. Looking at your data can help you pinpoint the problem, retarget the customers, and ultimately increase sales.
Predictive analytics also allows you to offer complimentary products to specific demographics. If male customers in their mid-30s frequently buy running shoes, send them target ads for weights, fitness trackers, and workout clothing.
Predictive Analytics: The Bottom Line
Predictive analytics can be complicated and it requires scrupulous research and data collection. However, the return on investment can be quite high when it is done correctly. Predictive analytics is not only useful, but completely necessary in the modern world.
With the massive amount of user data now readily available to every company, data-driven marketing is becoming the norm. Leveraging predictive analytics to create highly targeted campaigns can help your company not only stay afloat, but thrive.
Need some help? At LookinLA, we build data-driven marketing strategies for clients to help them become industry leaders. We have experience working with a diverse range of clients. You can read success stories here and reach out to book a call here to talk. We look forward to hearing from you.