A significant part of consumer data that businesses, cities, even government organizations own today includes some location intelligence. For the longest time, it was home and office address but today, mobile technologies have made location intelligence a dynamic, real time capability. LifeSight has been designed to empower all entities – private & public, education & non-profits, brands and financial organizations – to glean amazing insights and make informed decisions using location intelligence. And the good news is that LifeSight is already enabling this – conveniently and in a time-efficient manner – for tens of brands around the Asia Pacific region.
Often times, there will be multiple location components or additional factors within a single dataset. In these cases, there are many valuable questions to ask about how these location components or factors might impact one another, for example:
The challenge comes in when there the location intelligence in a single data set is drawn from multiple sources or there are additional factors at play. Then, the manner in which these sources impact one another becomes crucial information. We explain this below –
- For franchise-run retail outlets, brands could wonder if a new retail location could cannibalize customers from existing outlets in the vicinity.
- Real estate brands might be uncertain about the success of their specific projects, especially because the presence of competitor brands in the vicinity affects them.
- City authorities could wonder if a new public transport route would actually be effective for citizens or if it would just be redundant because of the lifestyle and habits of citizens on that route as well as existing infrastructure.
For any decision-maker, whether a corporate marketer or an urban planner, location intelligence is no longer enough for them to maximize ROI and make the best decisions. What they need now is the ability to deep dive into location techniques and analyze trends to answer specific questions. This is where a location predictive analytics model come into play.
What is location predictive analytics model?
In essence, this is the science and art of modeling how different factors and sources of location intelligence interact with each other. It is effective for examples listed above or rather, any business that gets affected by different components or sources of location intelligence.
In simple brand and retail marketing terms, this could simply mean a detailed deep dive into a consumer’s shopping habits, a map of his residential, workplace, and lifestyle locations, and where he goes before he shops or after. All of this, while maintaining the consumers’ privacy and anonymity – a win win proposition in marketing if there ever was one.
Another way to look at it is that consumer data may have components that don’t complement each other and in fact, repel each other! For example, a consumer goes to a skiing equipment outlet and then checks into a ski resort – complementary data sets. A consumer goes into an orthopedic surgeon for a duration that is likely reflective of a surgery. The chances then of him going to a sports equipment outlet are rather slim.
What we think is the future of location predictive analytics modeling in marketing
Simply put, it will change marketing as we know it. But for that, it will be important for analysts and marketers to understand that location predictive analytics model is not simply of map of consumers’ locations. It is an analytics model. So the goal when using the technique should not be limited mapping how consumers get from Point A to Point B or their behaviour during this journey. A sound location predictive analytics model – like the one that complements LifeSight – allows users to not only map locations but also predict the future. In brand marketing terms, this means that by analyzing location trends over time, marketers and analysts can actually predict where the consumer is headed. For example, if a consumer goes wine and cheese shopping before every long weekend, you could predict a lot about his social life or about his love for house parties, make your communication personalized when the next weekend rolls along. This will help your brand make an instant connection and find place in the consumers’ lifestyle.
Another use case of location predictive analytics model is beyond brand marketing. It has the potential to enter into the realm of strategic business decision-making too. For example, consider commercial or residential real estate developer. Using the predictive location analytics model, developers can gauge before they invest in the land exactly how attractive a new location will be for their target consumers. They could also already own pieces of land across the city and predictive location analytics modeling could help them understand which locations work for which price range, whether they work for commercial or residential projects, and if it is commercial, would a mall property bring in more ROI or an office space. These decisions are all dependent on how consumers interact with spaces around them – and hence location predictive analytics modeling can bring immense value to business decision-making.
Are you ready to get on your own location predictive analytics journey? Help us help you!