WPP and IPG’s weak recent quarters show that large company cuts to advertising budgets are beginning to hit traditional advertisers. P&G has announced that it will work with 50% fewer advertising agencies and Unilever has declared a 30% cut to its ad spend, signalling that the era of traditional, over-sized, broad-stroke ad campaigns is waning.
As advertising budgets get progressively slimmer, costly adverts in traditional media forms such as print and broadcasting have become less sustainable. Increasing numbers of large companies are moving their advertising online, as online marketing can be made visible to an increasingly large audience. But with the proliferation of social media has come deafening levels of advertising, and it is a lot harder for a brand to make itself heard over the surrounding noise. The 30% rise of adblocking extensions last year also means that invasive and poorly-targeted ads are having a knock-on effect on all online advertising. The ubiquity of online adverts, and their increasing perception as intrusive and annoying, means it is difficult for advertising to reach the right customers and have a real effect on revenue.
But rather than turning off from digital marketing altogether, marketers need to embrace new technology, using the data of existing customers to target marketing strategies to customers more closely, cutting through the marketing clutter. With loyalty cards, bricks-and-mortar retailers have access to exact data about which customers are making which purchases in-store, and when. Online retailers have information about website visits, product taste and demographic details such as age and gender. All this information can be used to target customers more specifically. But with great amounts of data come great amounts of work, and it is difficult to know which information is relevant and what can be done with it.
This is where AI and data analytics become crucial. Analysts can use vital data to parse a large undifferentiated population of existing customers into smaller segments with key common demographic and purchasing traits. Once identified and isolated, marketers can develop and personalise different marketing strategies to each group, such as combinations of discounts and promotions or targeted ads. Implementation of these strategies can be automated and carried out through multiple channels, such as Google Display Network, personalised emails, SMS and mobile push messaging.
But how can marketers find groups small enough for personalisation? Once segments of customers with similar known traits are isolated, each one can be further split into identical testing and control groups, and different marketing strategies tested on them to see which are most successful.
Following this, marketers can apply AI programmes to sort through and make observations on which strategies are most successful, and which subsections of a group have best responded to a particular strategy. Analysing these high-responding subsections, the programmes can note what specific traits they have in common, improving understanding of which data is useful, and which is not. The segment can then be narrowed down into even smaller personas based on how they respond to particular marketing techniques. In this way, marketers can use AI and data analytics to run marketing experiments while boosting revenue, to find out which strategies work for whom, and optimise possible revenue.
Adore Me is a rapidly-growing online lingerie brand based in New York City. Offering lingerie, bras, sleepwear and swimwear, Adore Me is quickly expanding within a market dominated by giants such as Victoria’s Secret and La Cenza.
Starting with only five distinct segments of its customer base, Adore Me utilised this method to further identify 66 distinct customer personas, to whom they could market in a more personalised fashion that suits the customers of each persona best. Within just a few months of starting this technique, the company increased its revenue by 15%.
Once marketing strategies are adapted to the specific tastes of certain types of consumers, marketers can use AI to help them work out how to best keep their previous customers coming back, creating a brand experience that will turn one-time buyers into loyal shoppers, and encourage already-loyal clients to buy more. This is a radical shift in the way advertising and marketing are conceived. Instead of focusing on attracting new customers, marketers can focus on the best way to generate maximum revenue from existing customers. Adobe estimates, for instance, that a 1% increase in returning customers raises revenue by 10%. This does not mean saying goodbye to new customers either. 74% of people say word-of-mouth is the most important factor in buying from a brand. Building brand relationships that keep customers coming back will bring in new customers automatically.
This kind of marketing experimentation is useful because it means marketing strategies can be continually improved and optimised, each experiment informed by the last. As time goes on, marketers can draw conclusions not only about specific types of customer and what appeals to them, but the way that this changes over time. Knowing no customer stays the same for ever, AI programmes can use data about the customer journey through and between different segments over time to predict how groups will develop in the future.
As giants like WPP begin to slow down in growth, it is becoming clear that more traditional forms of advertising are losing centre stage. But entering the storm of online advertising is not effective alone in creating positive brand awareness. To deal with the scale of modern marketing, businesses need to shift from the art of advertising to the science of data analytics.
Using data already collected to sort the customer base into groups, these groups should be individually targeted with a variety of marketing strategies. Marketers should use AI to automatically measure the revenue from each strategy upon each group, producing more focused, valuable data. From this, segments can be more and more thoroughly categorised, and the marketing techniques further adapted to what suits the customer. These approaches consolidate the customer base to generate more revenue and let it expand via word of mouth.
Using science and technology on a vast scale, advertising and marketing are being led into a new age.
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Read also:
Leveraging marketing personas: differences between ‘I want’ and ‘I need’
Audience profiling: what it is and how it can help your business
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Science-first relationship marketing hub, Optimove, is used by more than 250 customer-centric businesses to drive measurable growth by scaling customer engagement. Optimove combines the art of marketing with the science of data to autonomously generate actionable insight, empowering marketers to deliver highly effective, personalised, customer marketing campaigns across multiple channels. The company’s unique technology suite helps marketers maximise customer spend, engagement, retention and lifetime value.
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