The COVID-driven accelerated adoption of online services for everything ranging from food delivery to entertainment and grocery shopping seems set to continue. As businesses went digital and virtual customer interactions exploded, there was an accompanying acceleration in the growth of data in every transaction. This data containing customer preferences and habits is a treasure trove for advertisers if used right. When treated right, this data can offer marketers and brand owners rich insights on how to derive the most ROI from their marketing and advertising initiatives.
But handling such large caches of data is no easy task. This is why advertisers must move away from traditional methods for their strategic planning and incorporate big data analytics into everything from planning to executing campaigns.
Data analytics has been defined as “Data analytics (DA) is the process of examining data sets to find trends and draw conclusions about the information they contain. Increasingly data analytics is used with the aid of specialized systems and software.”
The promise is that the insights advertisers need will emerge through the considered application of Data Analytics practice. Let’s use this blog to lay out just how. Here is a list of the top 4 areas where big data can help advertisers re-define their campaign experience and win more conversions.
Identify customer needs
Knowing what a customer wants is the key to any successful product evolution, marketing campaign, or communication strategy. For a long time, marketing has been accused of advertising the product and not addressing real customer needs. With big data analytics, it is possible to identify particular customer behavior trends in disparate target segments to craft more impactful communications.
For instance, earlier, if advertisers wanted to target customers for a footwear brand, they had to rely on purchase history from the CRM and craft generic campaigns with wider appeal. Today, thanks to the growth of connected wearables, they could, potentially, identify a set of prospects who require extreme durability because they run more than 10,000 steps a day. Product owners could identify if this is a gap worth filling with their product. If so, brand communications to this audience could zero in on the product’s ability to handle strenuous physical activity. Data helps close the feedback loop to everyone’s benefit.
Creating personalized campaigns
Armed with more specific information about their customers, advertisers can create more meaningful campaigns that offer relevance and are timed “just right”.
For example, a retailer might anticipate a spike in demand for a specific style of clothes in a particular region in response to an external event (like a viral post on social media). As an advertising agency for the brand, this insight can create an uber-effective marketing strategy that focuses on the line of items in stock with extensions that make them specifically relevant to different target segments. This process can be further filtered to very granular levels like sending personalized emails to individuals with links to products in their desired color, price range, and preferred sizes. All these insights can be unearthed by applying big data analytics on the CRM and customer data from public sources, web browsing and engagement history, and previous transactions and trials. The right message at the right time drives closures and conversions.
Optimizing Campaigns with AI
Artificial Intelligence has already made an impact on the world, and the advertising industry is no different. With AI and Machine Learning addressing extremely large and complex data sets, it becomes possible to identify patterns, establish the impact of many diverse factors on the market, and make predictions that can help drive precise and comprehensive strategies.
AI can also use customer data from different enterprise systems to create personalized recommendations on product bundles and pricing and define the best way to deliver those messages to the intended target. AI can work without bias, identify deep signals from past customer interactions, and provide advertisers with data to further fine-tune their campaigns. For example, based on the purchase of baby products by a customer, AI algorithms could create a baby’s growth model. This could drive automated marketing emails with data about relevant products for the child as they grow, like toys, bicycles, skincare products, etc. This enables brands to create more meaningful campaigns that will catch the attention of customers.
In an increasingly digital world, for any marketing campaign, results largely mean conversions. Conversions could be of various types, including a click on a Call-to-Action button on a website, an app download, a coupon redemption, or an actual purchase from an online store. Advertisers can use big data and machine learning to improve all these conversion ratios significantly.
For example, on a web landing page, AI can identify exactly where to position an ad so that visitors are more likely to notice and click on it. AI could help define the ad message and format most likely to convert from a large selection set. AI can perform this analysis at pace and with precision. The AI algorithm looks at past data such as scroll time, mouse movement tracking, page sessions, etc., and marries that to real-time insights to drive timely actions that are likely to have a better impact. Higher conversions will automatically translate into better ROI for brands that partner with advertisers to promote their products.79% of business leaders opine that failing to embrace the trend of big data analytics will have severe consequences for their business. Marketing, brand communications, and advertising, especially in our newly digitized world, are among the most likely beneficiaries of this practice. For marketers, big data will be the key driver of communications with customers by creating more meaningful and data-driven campaigns that will ultimately build lasting relationships with customers.