By Piero Pavone, Co-Founder and Chief Operating Officer, MainAd
With the customer journey becoming ever more complex, the race is on to reach the right customer at the right time with the right messaging, all before the next-best brand comes along and steals the limelight. But how can this be achieved?
As self-development author Brian Tracy quite rightly asserts: “Concentrate on the activities of prospecting, presenting, and following-up; the sales will take care of themselves.” If we apply this to the context of digital marketing – where drawing in new customers is equally as important as retargeting online visitors (who are then up to 70% more likely to convert) – it is clear to see that both ‘prospecting’ and ‘following-up’ should form core elements of the overall marketing strategy.
However, as marketers continue to conduct an increasingly wide range of activities (email, display, paid, social or search) via multiple channels (video, desktop, mobile), the concern for many is whether prospecting and retargeting methods are conflictive, or complementary, to their own marketing efforts.
Intertwining prospecting and retargeting campaigns
Although prospecting and retargeting strategies focus on reaching separate segments of a brand’s target audience, the approach used to communicate with online users is actually very similar. Crucially, both methods rely on accessing, analyzing, and utilizing large volumes of data.
Given the increasing amount of customer data available today, AI technologies can be implemented to expedite the process of refining these large pools of data, helping marketers to determine previous user activity or interactions – either with the specific brand campaign or via other relevant channels – and better understand their preferences.
Machine learning tools can facilitate the prospecting process by holistically analyzing multiple layers of behavioral activity to streamline potential customer segments. Meanwhile, the same tools can supplement retargeting strategies by utilizing browsing history and previous interactions to re-engage the target customer.
In both scenarios, the insights derived are fundamental as they bring value to an otherwise overwhelming and potentially meaningless set of data. Ultimately, having the ability to determine the format or type of content to include in the marketing message being delivered to a particular individual can maximize engagement levels – without the need to run two completely independent campaigns.
For example, prospects can be served a version of an ad containing a broader message about a brand’s products or services based on their previous online activity; whereas existing customers – who provide a richer dataset to be fed into the machine learning platform – will be primed to engage with a specific message pertaining to a product they have previously bought or viewed.
Continuous campaign optimization through AI
Both prospecting and retargeting methods that incorporate machine learning technology can effectively ‘close the net’ by honing in on a single opportunity rather than relying on broader audience clusters. Individual users can be targeted based on an infinite combination of previous activities and preferences, whereas wider segments are by their very nature limited, as they relate to entire groups.
The technology continuously ‘learns’ from exposure to a user’s behaviour, allowing more specific pockets of inventory to be targeted according to marketing objectives, thus continually optimizing a brand campaign as it unfolds.
By combining the use of AI with ad bidding technology, advanced analytics can be generated and leveraged in real time, helping to speed up the process of prospecting or retargeting, respectively. Therefore, marketers can now truly benefit from delivering their ads on a programmatic scale no matter their target audience, and ultimately – by eliminating wastage and maximising conversion rates – marketers can rest assured that every impression counts.
Encouraging customer loyalty
One of the many drawbacks of a scattergun approach to marketing is inadvertently bombarding customers with repetitive ads for products they have already purchased. By utilizing real-time data, driven by machine learning platforms integrated into a powerful bidding tool, marketers can enhance rather than detract from the customer journey. Implementing techniques that can offer alternative product suggestions or advise of a complementary item that may be of interest will help to encourage brand loyalty from existing consumers.
Whether prospecting or retargeting, transparency is key in determining the location of a customer within the sales funnel at any given time – from those who are completely new to the brand, to users who have visited the site but are shopping around before they decide where to purchase, or individuals who have already purchased but have the potential to become valuable, long-term advocates of the brand.
By utilising big data and AI, marketers no longer have to choose from an ‘either-or’ approach when it comes to implementing prospecting and retargeting strategies into their campaigns. The fact that each approach is dependent on the other and the two are therefore inextricably linked means they both form an equally important role in the sales funnel and should be executed in tandem. Marketers that understand the insights derived from machine learning processes can use prospecting and retargeting as effective marketing tactics dependent on each individual user to make the most of the marketing mix.