How using artificial intelligence can help marketing teams increase their credibility

Ricky Thomas, CEO & Founder, AVORA
Ricky Thomas, CEO & Founder, AVORA

Knowing more about who you’re speaking to defines the ways you engage with them. This is one core sociological insight that unites marketers. Whether it’s media consumption habits, disposable income or general lexicon, one thing’s for sure – speaking to an audience is only effective if done in their language. This simple factor is precisely why the influx of data analytics has ultimately transformed marketing strategies for brands of all sizes. Enhanced interpretation of the information that’s readily available has enabled marketers to gain a complete and holistic overview of their audiences.

 

For instance, take Amazon’s suite of Alexa-enabled devices for example. Despite Amazon declaring that it doesn’t plan to open up its flagship AI-enabled Alexa devices to ads, the retail giant has essentially established its own content marketing channel. growth of. They’re seen as time-saving technology that’s become somewhat ubiquitous last year seeing ownership double within six months.

 

As more brands seek to harness the efficacy of interactive marketing to win over the hearts of consumers, the use of data processing in all aspects of marketing is only set to continue. Here are three reasons why artificial intelligence and machine learning can work to elevate the marketing function within C-suite discussions.

 

Cutting through the noise

 

Data-driven marketing has reached new heights in recent years. Spotify’s now infamous Wrapped campaign proves the rich narratives that only a few data points can paint. This portrait becomes all the more detailed when you factor in social insights, mobile data and even segment it by location, age and demographic. By using this multitude of data points and extracting specific insights by way of AI-powered tools and data analytics, marketing teams can speak to audiences on a more personal level than ever before.

 

The sheer wealth of this data can be overwhelming without the aid of machine learning processing tools. It can be difficult to corroborate so many different points to do with location, age, profession, buying habits, predicting wants and demands etc. What’s more, with testing being a thoroughly established practice within the world of digital marketing, non-technical marketers can be tempted to over-segment a data set. Implementing complex tests to systematically “prove” a proposed theory and speak to your audience in an overly nuanced fashion can lead to false interpretation and de-rail a campaign. In essence, you can get lost in the data – mesmerised by it.

 

Working to avoid assumptions and instead, acting on real first-hand insights associates your brand with an air of credibility. This tactic is vital in an age where brand integrity exists as a real factor in consumer purchasing decisions. According to a recent study cited in the Harvard Business Review, 64% of consumers who have established a relationship with a brand mention “shared values” as the main reason for that relationship. Essentially, if you want to make an impact with prospective customers, you have to align your brand with their values. This key revelation that an analytics-driven approach can enable brands to underpin the ‘why’ behind a complex purchasing decision as opposed to just the ‘how’ has prompted  many to sit up and take notice of the transformative impact that AI can have.

 

Seeing behind the metrics

 

Achieving this level of impact with a specific subset of your audience is the goal. By winning over tight-knit groups, you not only stand to acquire customers but convert them into true brand advocates that will recommend your business to their own sphere of influence over time. But how do you convey this success against a significantly larger pool of your target market?

 

Acting with such a level of precision from the offset requires continuation. Planning through one lens and reporting through another can lead to errors in the next phase of execution.  We can attribute success to the wrong factor and become too firmly attached to using one set of metrics. Ultimately, but if post-campaign analysis is constructed with broad strokes, these micro-victories will go unnoticed. AI is required in order to see beyond the expected outcomes and really pin-point the precise moment of an uplift in sales or engagement. If we can’t fully comprehend the ‘how’ of a campaign, it’s impossible to get to the ‘why’. And that is what’s vital to improving future performance.

 

Make messages go further

 

Moving forward from a campaign with real knowledge about why its succeeded puts marketers in a powerful position. Operating from a blueprint of success is the best way of working to really change perceptions of a brand and effectively ‘move the needle’ to create tangible growth. This is an approach that some of the biggest names in technology and FMCG have taken to ensure they’re getting the most out of their marketing spend.

 

Last year saw Unilever, Proctor & Gamble and Sky all take an axe to their digital budgets. Despite leadership citing many reasons, the overall strategic outcome here appears to be increased precision across their channels. When dealing with brands that aside from their mass appeal, exist across so many portals both online and offline, omnichannel ceases to be an aspiration and becomes a necessity. As a CMO, you need to understand purchasing habits both on a truly microscale and at volume to be able to dictate spend in a sustainable way.

 

Right now, we’re in an interesting time of shrinking budgets and growing audiences. The fluctuation of social channels is ongoing, establishing change as the only constant in our digital-first lives. This state of flux in the composition of our online identities leaves the window wide open for false positives and irregularities between datasets. AI-enabled tools can track customer conversion rates down to the penny and work to uncover the ‘why’ behind purchasing divisions. Machine learning’s very nature, to detect patterns, works to completely automate the discovery of anomalies leading to better practices in data integrity, resulting in credible marketing with better efficacy. What’s more, its work within reporting to derive actionable intelligence can transform the very framework of a business. Understanding just how far spend can be propelled is exactly what’s needed to support C-level decision-making processes.

 

By Ricky Thomas, CEO & Founder, AVORA



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