How to win at customer acquisition

Too many brands are relying on a ‘patchwork’ of methods and spending more on acquiring customers than they are worth.

How to win at customer acquisition

Jeremy Fain, CEO & Co-Founder of Cognitiv, tells Performance Marketing World how troves of customer data can be used efficiently and effectively to profitably acquire customers...

In order to create a successful customer acquisition strategy, marketers first must think about the customer journey, and the numerous branching paths that exist to the final destination. A decade ago, this was easier to do: e-commerce was not yet dominating retail, so there were a limited number of ways to learn about a product or make a purchase. For example, someone looking to buy a suitcase might see an ad on their way to work and decide to visit the nearest stockist for that product. Once there, they could talk to the salesperson to learn more, see what other models were available, and perhaps by the end of the shopping session, that person would have been persuaded to go through with the purchase. 

Nowadays, hardly anyone takes such a direct route to a purchase. The customer journey has become increasingly complex, thanks not only to the amount of information available to potential consumers, but also because of the complexity and breadth of digital advertising options. People are no longer just being served ads on billboards or on television; they are listening to ads embedded in podcasts, watching spots on their favorite streaming services, tapping through sponsored posts on social media, and so on. Instead of talking with a salesperson, consumers now rely on myriad review sites to provide the “unvarnished truth” about a product’s quality and reliability, and often make purchases without having interacted first-hand with a product. Everyone has a different path to purchase, which makes marketers’ jobs that much more difficult. 

Deep learning

Without a profitable customer acquisition strategy, companies will fail. Despite this, so many companies big and small, established and brand-new, end up relying on a patchwork of customer acquisition methods that are not only inefficient, but also ill-suited to the realities of the digital advertising landscape. They find themselves today spending more to gain a new customer than that customer is worth. It is time for brands to develop a comprehensive strategy that utilises the most advanced predictive technology on the market - deep learning - in order to drive customer acquisition while achieving the efficiencies that enable long-term profitability. 

The key to profitably acquiring customers today is personalised, efficient targeting. Since everyone’s customer journey is different, marketers need to gain an understanding of each person’s unique habits, values and preferences, as well as which touchpoints they are more likely to interact with. Traditional, one-size-fits-all customer acquisition models will not cut it anymore. A new, sophisticated approach is needed - one that uses deep learning to chart the best course for converting each individual customer.

As brands have expanded their online presences, they have also been able to acquire ever greater troves of data on their customers. By applying deep learning to these data sets, brands can learn more about how their customers’ shopping habits and preferences develop and change over time, as well as which types of consumers are most likely to engage with their messaging - and thus more likely to make a purchase. Deep learning-enabled algorithms learn autonomously, using the patterns they find within the data to make predictions about future behavior, and they are capable of teasing out subtle connections between people that, on the surface, might not have much in common. By identifying the subtle tells that mark someone out as a likely prospect, deep learning allows marketers to aim their advertising dollars squarely at the bullseye. 

Real-time algorithms

Because people’s online lives move so quickly, it is imperative for marketers to be able to optimise their advertising in real-time, or as near to it as they can get. Deep learning algorithms act as a continuous feedback loop, so every outcome - whether successful or not - is immediately fed back into the system and used to improve future predictions. The reality is that manual optimisation is never going to be able to achieve the level of accuracy or granularity required in order to get consistent, scalable results. Even if humans had the ability to comprehend and analyse vast amounts of data, we would never be able to execute as quickly or as accurately as a machine can. 

That said, deep learning requires investment and commitment in order to produce results. Some marketers have a tendency to jump ship if they feel results are not being seen quickly enough, but deep learning rewards patience. As the average length of the customer journey has increased, it has become imperative for brands to take a longer-term view of those customers, and be able to anticipate their needs in advance. Because deep learning algorithms are self-learning, the more time and data you give them to train, the better the algorithms will be at identifying the crucial points in a customer’s path to purchase and identifying clusters of consumers to target. 

Customer acquisition can be a complicated, expensive process, especially when relying on outdated tactics that require extensive oversight and continuous retooling. With deep learning, marketers can be strategic about how and where they spend their ad dollars, while relying on the technology’s real-time optimisation capabilities to provide accuracy, efficiency and scale. Brands need to step up their customer acquisition game if they want to survive - and deep learning is just the tool to help them do it.


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