There’s so much you can learn from your customers’ digital footprints – including how much to spend and where, to maximise business returns.
Imagine you are the Brand Manager of a Fortune 500 automotive brand planning to penetrate a new market in Southeast Asia and to set up showrooms.
Anyone with a rudimentary knowledge of the local market can tell you which areas all the luxury or affordable automobile showrooms are clustered. So, your first instinct might be to set up shop there, since that particular area is known for having a concentration of car dealerships.
While this isn’t a bad strategy, per se, but maybe we can do better. What if you had data on which income group your buyers belong to? That is certainly a useful piece of information. You could leverage that information to place your outdoor ads like billboards and roadshows, in affluent parts of town – to promote luxury automobiles from Germany, the UK, or the US.
And if you are selling competitively priced cars from Japan, you would probably want to place your showrooms, outdoor advertising, and promotional activities in the middle-income parts of town. Another consideration:
Data-driven insights are not always intuitive, sometimes the truth can catch most battle-hardened marketers off-guard. For example, we recently found out through data analysis that a reasonably priced Japanese brand has more market share from the affluent parts of society, as opposed to the average Joe with a median salary. Seems counter-intuitive, but true, nonetheless.
When compared to this reasonably priced brand, higher-priced Japanese manufacturers have lower affluent market share and higher middle-income market share. In other words, middle- and lower-income groups prefer pricier cars when it comes to Japanese brands, and affluent groups prefer cars that are more reasonably priced!
Now let’s say the marketer has already established a presence in the region and has strategically placed showrooms at seemingly sensible locations. What can data do for them now? Or to be precise, how can data help them predict and plan for foot traffic?
With ADA’s proprietary telco-powered Data Management Platform (DMP), XACT, we are able to access footfall data from customers of all ages, demographics, interest groups, and beyond. It is a fairly large sample size with high variability and rich history, which we can leverage in the form of insights and predictions.
So, if you are trying to anticipate the volume of foot traffic on certain days and hours, certain locations, or even under specific weather conditions, we can create highly accurate simulations from machine learning and data-driven predictions.
Contact us and we can have a quick chat.
Let’s say you want to market luxury hand-crafted Oxford shoes that are only affordable to the affluent segment. When you create a campaign strategy to increase footfall, it would come in handy to know which malls they typically flock to when they plan to shop for shoes.
Combine consumer’s search history data (example: “Oxford shoes”), with their physical location data, and you can zero-in on the areas where your most imminent customers are likely to be found.
For example, say you plan to increase foot traffic on October 10, 2019 – during the launch of your new line of formal shoes – where you can plan if you know the time window between purchase intent and actual purchase, which could be several hours to several days, even several months in some cases.
Let’s say the window turns out to be three days before, i.e. we find out that people are most likely to search for shoes three days before they make the actual purchase.
These powerful insights will help you to plan and advertise online and launch promotional activities – in areas where your consumers are hanging out, both virtually and physically – three days ahead of October 10!
Are these type of insights you’d be looking for your business?
For a short period, it’ll be our pleasure to show you a demo of this brilliant tool!
Vittorio leads Business Insights, a team dedicated to providing data driven solutions to gain positive business outcomes. Working closely with data analysts, data scientists, business consultants and industry experts, he uses artificial intelligence and machine learning, builds data lakes and analytics centres of excellence – with the aim to drive better decision making in the corporate world.
As a management consultant, he has worked with clients to define and implement their digital, data and analytics strategy. Among his accomplishments, he has helped banks to drastically improve customer experience, telcos to make use of their data and education providers to improve students’ performance.
He came to Malaysia to drive the growth of EY Data and Analytics where he created a full-fledged practice offering strategy, data science & engineering, and big data solutions.
On a personal note, he is a strong believer of “mens sana in corpore sano” – a Krav Maga practitioner, a trail runner and a biker even. A global citizen, he is fluent in English and Italian – has lived in Europe, UK, Middle East, Australia, and New Zealand and holds impressive credentials from Computer Science degrees to MBAs.