Among the plentiful tech acquisitions of 2016 there were several that puzzled and intrigued the industry. But it’s safe to say that when the strategic rationale behind M&A activity in the tech space is initially unclear, it’s usually about data – and more specifically, first-party data modeling.
When Oracle kicked off the year with the acquisition of AddThis, it was to incorporate the social bookmarking company’s vast quantity of social and site signals into its own data cloud. And when Salesforce rounded out a busy year of M&A with the acquisition of DMP Krux, the goal was both to improve its data management capabilities and to integrate Krux’s data set into its audience targeting platform.
With the amalgamation of distinct data sets providing huge potential for ad targeting, companies seeking new growth opportunities and varied revenue streams realise the value of quality data. But some companies are ill-prepared to deal with the overload of information this type of M&A activity can bring, and there is a risk they won’t be able to unlock the full potential of the data they have paid for.
So how can data sets produced by M&A activity be best used to drive personalised, relevant advertising experiences?
Use acquired data sets to enrich first-party data
The goal of data-driven M&A shouldn’t be simply to gain large volumes of data, but rather used to enhance and enrich the company’s own first-party data. When Microsoft closed its purchase of LinkedIn in December it was largely to access the enterprise social network’s unique data sets and gain deeper insight into the professional community. But to make the most of this acquisition and create monetisation opportunities, Microsoft must link the user data from its own products – such as Office 365 – with social and content consumption data from those same users’ LinkedIn profiles.
Equally, when Time Inc. bought Viant earlier in the year it was with the intention of combining quality content and data to win over a share of the ad dollars currently going to the likes of Facebook. To achieve this Time Inc. must link up its own first-party data – including the email and physical addresses of its print and email subscribers – with Viant’s deterministic data set, as well as data from its one billion MySpace accounts. The combination of Time Inc. and Viant user bases will allow precise audience targeting. Viant also recently announced the acquisition of mobile and cross-device targeting company Adelphic, with the aim of creating a people-based platform capable of delivering highly personalised messaging and competing with the reach of the tech giants.
Make the most of your customer interactions by understanding the relationship between audience and content
In addition to integrating acquired data sets with their own first-party data, companies must also look to artificial intelligence and cognitive technologies to better understand the relationship between audiences and content. Semantic analysis technologies such as Natural Language Processing can read digital content just as humans do, producing a precise conceptual map of the content on a page that illustrates the main topics, the emotions the content evokes, and the sentiment of the text. This type of insight enables a deeper understanding of the audiences consuming content and their interactions with the campaign messages, and uncovers communities of interest that exist beyond traditional demographic or geographic boundaries.
Build propensity profiles for advanced targeting
Using enriched first-party data, as well as insight into audience interests and online content consumption, highly actionable 360-degree customer profiles can be created as a base for personalisation and audience targeting. Using machine learning trained by customer interactions, this data can be used to predict what customers are likely to want in the future, and when they are likely to need it, determining a customer’s propensity to interact with specific messaging and ultimately to convert. This level of insight allows advertisers to deliver proactive, relevant experiences, targeting customers and connecting them at the right moment with products or services they already have the propensity to buy, without being intrusive or raising privacy concerns.
M&A activity in the tech sector shows no sign of slowing down as companies strive to compete with tech giants like Facebook and Google for advertising dollars. To make best use of the data gained through acquisitions, companies must look to enrich their own first-party data and use artificial intelligence to understand consumption – creating detailed propensity profiles that will inform the delivery of personalised, relevant customer experiences.
There will be many more tech acquisitions over the coming year, and you can be sure the majority will revolve around data.