More customers, more problems? That doesn't sound right
Traditionally, customer acquisition strategies were guided by industry best practices and experiential knowledge held by an organization’s executives and senior-level individuals. Sometimes these strategies were successful, sometimes they were not.
Success was primarily determined by the return on investment (ROI) for various acquisition initiatives. While ROI is still one of the best ways to gauge the effectiveness of your efforts, the “trial and error” approach can have severe impacts on revenue growth, especially for small and midsize companies.
Machine learning, a subfield of AI, enables companies to take much of the guesswork out of the customer acquisition process, and it all starts with good data.
B2C companies have access to an abundance of consumer data — data that enable companies to draw deeper insights about what their best customers look like, which leads are worth targeting, which types of promotions are likely to keep them engaged throughout the buying process, and just about any other outcome companies want to optimize.
As companies consider more data, they require more efficient means of analyzing it. That’s where AI becomes invaluable, as it allows companies to draw deep, data-driven insights and make accurate predictions about target outcomes. However, the key is implementation, leveraging these insights and predictions efficiently throughout the acquisition process.
3 applications of AI for B2C customer acquisition
1) Propensity modeling
The best way to optimize any customer acquisition initiative is to focus your resources on individuals with the highest propensity to convert on a target outcome. Why waste your precious marketing dollars on leads that probably won’t become customers, or worse, spend more on acquiring a new customer than they spend on your products or services?
Through various machine learning techniques, data scientists can build models capable of predicting — with a high degree of accuracy — a lead’s propensity to convert on a target outcome. These propensity models are especially helpful in optimizing lead generation and customer acquisition campaigns in two ways:
Predictive targeting: Propensity models “score” individuals on the likelihood that they will convert on an outcome. Outcomes can be as broad as “make a purchase” or as specific as “bought product X and product Y, but not product Z.” Propensity scores become actionable when they’re fed back into a CRM, ESP, e-commerce platform, or any other marketing system. As you might imagine, targeting likely-to-convert audiences helps drive conversion rates and ultimately improve ROI on acquisition initiatives.
Personalization: You achieve the greatest performance improvements when targeting propensity-based audiences with highly-personalized campaigns. Identifying the most influential variables in the modeling process helps you draw qualitative insights, which you can leverage to personalize and optimize qualitative aspects of your acquisition strategy: product photography, ad creative, web copy, etc.
2) Real-time lead scoring
The sooner you identify an individual’s propensity to convert, the better you can allocate your resources. Real-time scoring integrations feed new lead data into propensity models, where they’re scored and returned to various marketing systems. Real-time lead scoring can help you optimize your customer acquisition strategy in three ways:
Immediately determine whether the lead is worth further marketing or sales engagement. If not, save your resources for high-scoring leads.
Immediately enroll high-scoring leads into the right nurture campaigns. If a likely-to-purchase lead was captured on a particular product page, you might want to enroll them into a promotional campaign for that product.
Immediately inform your sales team when high-scoring leads enter the funnel. This is especially useful when using multiple propensity models for multiple outcomes. If a new lead has a higher propensity to purchase product X than product Y, your sales team should know as soon as possible.
3) Location intelligence
Location intelligence situates business data in a geographic context. Identifying "hotspots" of high-scoring individuals can significantly reduce customer acquisition costs in two ways:
Retail site and showroom expansion: Physical, or "brick-and-mortar," locations carry tremendous costs. It's no secret that location is paramount to a site's success. With scored geographies and careful analysis, you can identify the best (and worst) locations for new sites.
Service area geotargeting: Whether the goal is to drive foot traffic to physical sites or to maximize ROI on direct mail or canvassing campaigns, targeting spatially-clustered areas of high-scoring individuals will increase conversion rates and lower acquisition costs.
Where to start
Regardless of which application of AI is most relevant to your business, start by acquiring rich, high-quality data, and lots of it. Good training data fuels the models that give you the insights and predictions needed to optimize your acquisition strategy.
When you have the right data, and enough of it, you’re ready to train your AI. You’ll need experienced data scientists and a solid machine learning engine to build models that actually work, but once they’re built and predicting accurately, you’re ready to start optimizing.
For a deeper dive into data discovery and machine learning, download our Customer lifecycle optimization whitepaper. It lays out the four-step process that hundreds of B2C companies use to operationalize revenue-building AI, from customer acquisition to retention.