From dynamic web content to targeted email campaigns to mobile app push notifications, predictive marketing can take on many forms. However, they are all defined by anticipating future needs and interests based on customer data and history in order to shape online experiences for a higher degree of marketing success.
The following four predictive marketing mistakes can frequently occur and negatively impact the campaigns of companies across all industries, as well as the resulting opinions of potential customers. However, solutions are possible so long as businesses are aware of these mistakes, their causes and the ways in which to both prevent and correct errors.
1. Poorly Structuring a Predictive Model
Pulling a large amount of detailed data can give a business the chance to understand the history and interests of individual customers to predict their future needs. However, accessing massive amounts of vital data is only half of the equation when creating a successful predictive marketing campaign. Without a properly researched and structured predictive model, a company may be unable to provide the level of detail and nuanced experiences needed for success. Without proper planning, models may even provide incorrect experiences and product offers.
Solution: As discussed by Target Marketing Mag, successful predictive models prioritize detailed integration of customer data long before they begin applying these insights to the experiences they create. This includes collecting and comparing data from multiple sources so that a single customer view can be created, which not only creates a robust view of individuals, but eliminates errors, such as misspelled names or incorrect assumptions regarding interests. When combined with a predictive score that determines a customer’s level of interest in both product and the business as a whole, a predictive model can have a much lower chance of making critical errors.
2. Starting with a High Profile Project
Every predictive marketing campaign has to start somewhere and after investing time and resources into the systems that enable these targeted campaigns, it may be tempting to kick things off with a high profile, large audience project. However, the cost of error becomes higher the larger a predictive marketing campaign becomes, and it may take some time to smooth out any shortcomings that may affect a campaign.
As highlighted by Computer World, companies had frequently claimed that their predictive models will “revolutionize” their respective industries and, as such, launch high profile campaigns at launch in an effort to wow their target audiences. However, the projects may lead to well-publicized errors and simply be too large and complex to maintain over time, leading to businesses shutting down the projects that they claimed would propel them to the top of the industry.
Solution: Create small, realistic goals for projects that can make a difference in profits but will not have a major negative impact in the case of mistakes. Once successful, build additional, larger projects informed by past successes and failures.
3. Over-Targeting the Customer
Every predictive marketing campaign must walk a fine line between providing an individualized experience and allowing audiences to feel independent in their customer journeys. When a prospect has been over-targeted, he or she may become numb to your company’s marketing efforts or, even worse, begin to resent your business. This includes a constant stream of emails, continually reflecting their shopping history in all online experiences, addressing them by name everywhere and continually shifting website layouts in an attempt to match their interests.
While these experiences can help a customer feel understood by a company, they can also burn them out if they feel continually tracked while online. As customers become warier regarding how companies use their data, an over-abundance of obviously targeted experiences may cause them to seek out competitors.
Solution: A successful predictive marketing campaign should lay out a friendly path before the targeted customer, providing him or her with the products most likely to align with their interests for ease of access and customer support that quickly meets their needs. Consider less obviously targeted experiences and fewer direct communications so that customers feel more at ease with your company handling their data.
4. Leveraging Inappropriate Data
In the modern age of data gathering, many businesses have access to social media, email, shopping history and much more willingly provided by target audiences. However, the trouble begins when a company decides to leverage as much information as they can without discernment regarding whether or not they should put such information to use.
Often, these issues come down to the generic application of big data without crucial segmentation regarding marketing efforts. The result can be wedding congratulations to unmarried targets and baby product advertisements to prospects who haven’t actually announced their pregnancy yet. As discussed by Umbel, it may be tempting for businesses to collect all the data that they can, but the result is often a messy blend of crucial information and bits of insight that should never be used in marketing material.
Solution: When creating parameters for data collection, create clear objectives. What do you plan on using the data for within your marketing campaigns? In addition, what types of data will never be necessary within your predictive marketing? When combined with a system that segments this data as needed, companies can avoid serious mistakes.