Predictive Targeting: Tailor your homepage for each visitor
Predictive targeting analysis is a method of applying a page model from data collected upstream. It’s about learning from your business experience so that you can predict future events, like customer actions. It is a tool that can be very powerful.
Predictive analytics improves your website’s efficiency and conversion rate, which contributes to your business’s success.
What is that?
It all starts with visitor segmentation, which aims to identify the right target for each action you want to take. Then, with predictive targeting, you entrust the segmentation of your visitors to a learning algorithm that feeds on all your visitor data.
By analyzing visitor data, the algorithm develops an ability to predict the intention of your visitors. It also detects the correlations between them using micro-signs linked to their data, making it possible to adapt the experience according to each user’s profile and deliver on your home page the information they are looking for.
The predictive targeting algorithm is therefore capable of:
- Detect the visitor’s sensitivity to different messages to show him the formula that will make him convert. For example, suppose some visitors are sensitive to the effect of scarcity. In that case, they will see “Limited edition of 5 copies” appear, while if the algorithm detects that the visitor is price sensitive, it will display “Best price observed over the last six months”.
- Detect an appetite for one product rather than another. For example, on a site that sells both coats and swimwear, you can highlight the swimwear category on the home page to those interested, avoiding showing them coats. But, of course, the reverse will also be possible.
- Calculate the probability of belonging to a segment that cannot be defined or modeled a priori manually. Then, the algorithm can detect who the visitors of the “undecided” segment are, and the homepage will build accordingly.
How does it work?
Targeting is the study of what your customers are doing. Campaigns can be targeted based upon a variety of customer activities, including:
- Visits to your website (frequency and duration)
- Site navigation, specific clicks, and page views
- Searches on site
- Items purchased (and vice versa, abandoned baskets)
- Entering customer data in online forms
- Technical information (i.e., material used, type of browser, and geolocation)
- Customer value (total amount purchased)
Thanks to predictive analysis, you can identify specific market segments, evaluate customers according to the expected response and adapt your home page accordingly.
Taking all of these types of data into account will give you a 360 ° view of your customer, allowing you to provide them with exactly what they want and need at the right time.
What are the advantages of predictive targeting?
First, predictive targeting will help you significantly increase your ROI: with more precise targeting of the audience, it is probable to set up marketing activities that meet the needs of your visitors.
Then, you will improve the user experience by targeting only visitors interested in your offer. Thus, you offer a consistent experience to qualified prospects while taking care not to degrade the journey of other Internet users who are not concerned.
You will save time: Another significant advantage is that predictive algorithms are implemented very quickly and automate all your actions, saving you a lot of time.
You will get to know your customers: predictive targeting allows you to “zoom in” on each segment of your audience, and therefore to understand who your visitors are, and especially who are those who convert the best.
You will gain a competitive advantage: Predictive targeting can allow your site to gain speed and agility, translating into a competitive advantage.
You will optimize marketing productivity: With predictive analytics, marketers can identify insignificant trends and values and highlight critical scenarios that lead to better conversion rates.
Behavioral targeting is an effective way to connect with more qualified prospects simply by tapping into historical and contextual data. With so many information sources, it is easier than ever to build a comprehensive and consistent profile and turn that knowledge into marketing action.
In the years to come, predictive analytics and targeting will become more potent as e-learning improves. However, B2B and B2C companies can gain a competitive advantage at this early stage of development by embracing it now.