Sherlock.ai has been constantly getting better at helping clients reach out to not only their target audiences but ‘high-intent’ target audiences or those who are most likely to convert into their customers. This is done by analyzing and understanding different datasets – Visitation Data, User Behaviours, Purchases, and Digital Footprints.
Visitation – As the name implies, this dataset gives access to which specific types of places are the people visiting. Are they going to automobile showrooms, theaters, apparel stores, salons, or beaches repetitively? How much time do they spend in these respective places and what kind of activities do they take up while there? Also, what time of the day are they visiting these places? In the case of branded products and services, their brand affinities also become a part of the datasets. Once we know the type of places they have been visiting, we are able to predict their behaviour on the basis of that. For example, in a case study around the coffee shops in Mumbai to determine the best places to open coffee shops here on the basis of the prosperity index, the number of people visiting these locations, and their visit timings. The map of Mumbai was broken into blocks of different colours, each indicating the prosperity index of the area – the darker the colour of the block the more prosperous the area is. All locations of all coffee stores in Mumbai were also plotted on it including Starbucks, CCD, Barista, etc and overlaid them on the prosperity index. The map also gives the revenue potential of these stores. It also indicates the number of people visiting that store in a time frame and which stores are clearly doing well, which are average, and which aren’t performing well. One could also figure out the number of people picking up coffee as a takeaway and those who were staying in the coffee shop to drink. The map also has circles indicating the colleges in these areas and the enrollment number of each of these colleges. In some of these colleges with a lot of enrollments, there is no coffee chain. This data could help those looking to set up stores or kiosks. Coffee chains can use all this data to determine the location of their next coffee shop.
Digital footprints – When people browse the internet in various ways and using various tools, they leave behind digital footprints which are immensely valuable to marketers and companies selling goods and services. The digital footprints provide insights into what people are searching online, and the kind of apps they download and use – gaming, shopping, luxury, grocery etc. All these provide insights into their online buying behaviours. Post-pandemic digital sales have skyrocketed and these insights help in shaping the consumer strategy and reaching out to the TA with the most intent of buying their wares. Additionally, one can also see how social media reflects their behaviour.
Purchases – The next dataset which is of big help is how the purchase behaviours of customers are like – Are they impulsive buyers or do they research and buy or do they shop for discounts before picking a product. What segments are they spending on? If they are spending on branded things or does branded retail not matter to them? Additionally, a lot of intel is also gathered about the customers by working with credit card companies & Point-of-Sale cos, as their spending patterns can be picked up from here – cash sales, credit cards, debit cards, e-wallets, etc.
User behaviors – Combining the purchase patterns of customers with other indicators like the place they live, their spending capacity, where they work, what are their travel routes every day and their mode of transport; if they own bikes or cars; what is the number of members in their family; the prosperity index of the place they live in – all these help arrive at a holistic view of the consumer.
All the above datasets helps optimize their marketing spending as they are able to cut out all the fluff while trying to reach out to people. They are now armed with the knowledge which helps them reach out to those with the intent to buy. These are also people who match their brand’s profile and are most likely to convert into customers.