How Brands are Investing in Data to Create Impactful Ads
Data helps in accurately profiling the audience based on metrics such as gender, age, geography, occupation, likes, tastes, preferences and so on. This microscopic audience profile analysis helps in creating audience-centric campaigns and making marketing a science fully backed by quantitative results. Think about how Reliance Jio would have assessed the data consumption habits of millions of Indians in tier II and III towns, to come up with tailor-made offers for telecom subscribers in target cities.
Another example is that of Very.co.uk. Leveraging the power of personalisation, Very.co.uk engaged customers and delivered an impactful marketing message. Through its homepage banner, the company used customer information and weather data to recommend products matching the weather in the customer’s location. Apart from targeting data with respect to the weather, the company also personalised the banner with the user/visitor’s name. As Internet Retailing reports, at the time, the company was able to display 1.2 million variations of the above page, each of them fuelled by data to make it more relevant to every customer.
Understanding user behaviour
For a trained human, analysing the behaviour of four to five consumers could be cumbersome. When we talk about a pool of millions of potential audiences, the need is for a more quantitative method. Data analytics helps in connecting the dots through sparse data points. Netflix is a great example of a global brand that uses big data analytics for targeted advertising. With over 100 million subscribers, the company collects humongous data, which is the key to achieving the leadership position that the company enjoys. It rightly captures customer sentiment in order to provide them with relevant suggestions as to what to watch next.
Audience profiling, targeting, and historical trends also allow marketers to optimise their media buying strategy. They can leave out the least effective platforms and invest most of the pie in platforms that suit their campaigns or audience. This rejig helps in cost control as well as making the campaign relevant. This also takes off the burden of manually tracking the campaign progress and reallocating budgets.
A big limitation of traditional marketing campaigns is the inability to convert cold leads into business. Data-backed digital marketing can track page visitors, viewers of posts, those who download a certain report, click a link, the bounce rate, etc, and follow the steps for retargeting the campaign towards such users in the subsequent phases. This means chasing more qualified leads which results in higher campaign ROI and business profitability. The best example of data-driven marketing based on historic data is how consumers surfing the web or ‘chilling’ on social media see super-targeted ads that are relevant to them.
Campaign performance metrics are a good starting point to understand how the efforts pan out. With tons of data flowing in the background, an analytics dashboard represents a bird’s eye view of the campaign, showcasing audience behaviour and impact on a single page.
While data-driven marketing campaigns have better relevance, measurability and ROI, it is important for marketers to not leave too much to algorithms. It is important to have a human context and validation to every data point. Moreover, data analytics is not a quick fix solution. It takes sustained efforts, constant feedback and re-adjustments to produce a successful data-powered digital marketing campaign.
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