In order to drive strategic growth you need to create a dynamic data-driven customer acquisition strategy and implement it continuously. But with the diversification of the digital landscape, acquisition challenges are steadily growing. During the last five years, the cost of acquiring new customers (CAC) has increased by over 50%.

Many factors contribute to this reality:

• The customer journey and sales funnel are no longer linear, and marketers need to contend with an ever-changing funnel happening across multiple touchpoints. Getting to grips with customer intent is a constant battle.
• Fierce competition over the same audience pool in a saturated market is constantly pushing up bid prices.
• Marketing is happening in the now: opportunities and threats arise and subside on a daily basis.
• Customers’ expectations for responsivity, personalization and relevance are continuously on the rise.
• Ad networks and publishers are constantly changing their algorithms to create a more competitive content discovery landscape.
• GDPR and other data protection legislation has further limited marketing team’s power over their data.
• The technologies built to mitigate all these difficulties are becoming increasingly complex.

Most companies, however, are resting on their marketing laurels. Marketers are sticking to the acquisition strategy that served them well in the past despite the overwhelming evidence that the landscape has changed.

These changes spell out the fact that if you keep implementing the same acquisition strategy you had in place a year ago, at some point your acquisition math will break.

Improving customer acquisition

One of the keys to acquiring new customers is seeking them out where you haven’t previously looked. In other words, diversifying your audience targeting strategy by finding new channels and segments. With a shift in your strategy, you'll be getting your message out to entirely new groups of people. By creating hypotheses about new segments, and agily testing and validating them, you can expose new valuable audiences without spending all your marketing budget trying.

Finding new high-potential audiences can become a significant growth engine for your business, but it also requires overcoming a host of data and friction challenges:

• General data problems. Collection and analysis may fall prey to too much data, not enough data, stale data, or just the wrong kind of data altogether.
• Multiple channel monitoring. Collecting and analyzing data from the multitude of existing channels can be incredibly time-consuming when done on an ongoing basis.
• The ‘black box’ problem. Data can turn up results that show what is happening, without the information needed to understand why. Lookalike audiences is a good case in point: while it may enable you to find new valuable audiences, their data and attributes are concealed inside the publisher’s walled garden, preventing you from formulation any understanding about them or engaging with them on other platforms.
• Lack of ability for proper attribution. Even if you manage to expose new potential audiences and target them, it’s usually almost impossible to determine which engagement was the deciding factor for the purchase.
• Dependency on agencies and publishers. Having to depend on agencies and publishers for data can lead to a whole host of issues. Mainly, the lack of direct access to your audience data makes it harder to be agile and to generate significant insights based on that data.
• Affirmation bias. Even marketers fall prey to the self fulfilling prophecy syndrome, which compels them to act on their preconceptions and then find the evidence to back them up. This cognitive barrier prevents the exploration of new ideas and angles that fall outside our natural assumptions, keeping us from discovering the opportunities that often lie smack in the territory of our unknown unknowns.

The data paradigm shift

It’s clear that winning at the acquisition game requires a paradigm shift regarding audience data. Brands need to work with data that is:

• Fresh. Your brand’s audiences are not a monolithic mass. Their interests, affinities, preferences, intents and places of digital habitat are in a constant flux. To keep relevant you need to base you strategy on fresh data, that is real-time or near real-time. If you’re working with an audience analysis map that’s months, weeks or even days old — you’re throwing money to the wind.
• Multi-source. Data from a single platform is inevitably skewed, and, what’s even worse, it’s usually applicable only on the source platform. When working over an orchestra of platforms and channels, you need objective, platform-agnostic audience data.
• Brand-centric. While most platforms offer robust analysis and targeting tools, you always need to start narrowing down your possibilities from an infinite number of audiences to the segment that are relevant to your brand. Data that starts out with your unique brand, products and competitive landscape will decrease the complexity of your research by orders of magnitude, immediately zooming in on the audiences inherently most relevant to you.

To summarize, marketers need to put in place an agile audience analysis process based on brand-centric, real-time, multi-source data.

Today, only this kind of data can give brands the power to implement an agile customer acquisition strategy, bypass the barriers built by the search and social network titans, test and validate new audiences on an ongoing basis, and stay relevant and personalized in a world of dynamic and discerning customers.

Putting it simply, only this kind of data can bring down your CAC and put your customer acquisition strategy back on track.