I believe that the essence of any successful marketing strategy is timing and there can be no better time than targeting a customer at the very moment (well almost) when he / she is looking for something or expressing an intention which your company is well-positioned to fulfill. This situation is ideal for marketers to leverage the digital medium to provide, what I call, ‘the personalized M-R-C’ framework (messaging - recommendations - content) to such a customer, which could significantly improve a company’s chances of making a successful sale.
However, many organizations still struggle to make the most of this strategy. That’s because they are unable to leverage Big Data. Here’s where the social media top-up can help.
Marketers have traditionally relied upon campaign lists from third-party vendors in the hope of gaining either – a prospect customer database or some additional information on their existing customer base. These lists have challenges in terms of staleness and inadequacy of data.
Enter social media targeting. In the context of direct marketing, this provides companies / marketers with a consistent pipeline of ‘live’ and ‘well-mined’ customers – both of whom could help give a significant lift to a company’s campaign RoI as well as overall revenues. It seeks to do this by exploiting the massive data-ecosystem (Big Data) to identify and present on a regular basis the lists of both ‘live’ (in the market, at present) and ‘well-mined’ (highly curated ) customers / prospects.
Let us delve a little deeper into both the customer sets to get a better understanding.
Marketers can leverage the concept of ‘Fast Data’ – streaming data on an almost continual basis in the form of social media posts, click-stream data, purchase transactions, sensor data, mobile apps data, and the almost real-time processing of such Big Data - to generate insights for immediate action.
Fast Data seeks to identify the purchase intent as it occurs by aggregating event and behavior-influenced data. Let’s take an example: Social media posts such as “On our way to becoming proud parents” or “Excited about my upcoming dream vacation”, if served in real-time to companies whose products / services align well with the expressed customer sentiment, could truly represent the coming-of-age for direct marketing. These real-time feeds could leverage both the existing customer base as well as prospects with the underlying USP of in-the-market now or live customers who are, or have very recently, given indications about their purchase intents.
For generating the above-mentioned customer data, this technique begins by integrating the enterprise data comprising CRM data, transactional records, and demographic data into the marketing database to provide a single-customer view versus the earlier fragmented customer view.
It then seeks to build upon the customer profile by sourcing and adding external third-party data (from specialist data aggregation firms and public data sources) which provide additional customer attributes on both demographics as well as financial information (tax filings, credit scores, and so on). This data enrichment helps in a better and deeper understanding of the customer, and enables companies to undertake more personalized campaigns.
The highlight of this technique being its ability to harness Big Data to provide insights into the preferences, interests, and attitudes of customers, which is achieved through an ensemble approach comprising mining of different data sources such as:
These varied and rich sources of data help develop a holistic view of the customer. The insights generated translate into more personalized campaigns, which enable a deeper engagement with customers and prospects, thereby improving the possibility of a new or repeat sale.
Marketing efforts were earlier limited to working around the internal data of the company, at times embellishing it with list-buying from third-party vendors (some of whom were of dubious provenance). On the other hand, this technique offers a radical new approach by seeking to mine massive and disparate datasets (Big Data) with the objective to find qualified prospects that are actively looking to purchase in the market now for what a company is selling. It also seeks to provide a true 360 degree view of the customers by harvesting data from internal, external as well as Big Data sources to provide relevant marketing insights about customers / prospects.
Finally, I think imbibing all this data may just end up making direct marketing campaigns more productive and help take marketing efforts to the next level.
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