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Increase the effectiveness of your online marketing with in-store data

by Anh H. Nguyen

Companies are dedicating more and more resources to online marketing every year

According to the U.S Small Business Administration, 7% to 8% of a company's gross revenue should be spent on marketing. Depending on specifics, this could even go up to 10%. And the general rule of thumb is that at least half of this figure should go towards digital strategies. Companies that fall short of this benchmark often find their ROI less than satisfying. But solidifying the budget is only the first step towards maximizing your online marketing potential.

Online marketing has grown into its own industry with the average salary for a manager being US $61000/year. There are plenty of categories for a company to explore when it comes to the digital world : growing SEO, blogging, email marketing, video content creation, online tools, webinars, infographics, social media,... It is easy to get lost in such a myriad of options. Companies that take an empirical approach to business strive to measure the effectiveness of their online marketing, often by counting leads at fixed intervals to see which avenues bring in the highest ROI. They could then use those data to prioritize their online marketing efforts going forward.

The online territory is vast, growing, and full of potential.

While this method works to a degree, retailers using it face three inherent problems

First of all, if a retailer employs more than two channels (for example, Facebook ads, email newsletter, and Google ads) for a campaign, it would be difficult to distinguish which one is the most helpful bringing in leads. While some retailers try to overcome this hurdle by asking their customers some variations of "How did you hear about us?", the results are limited. The influx of online information bombarding our psyche every moment of the day makes it sort of challenging to remember exactly the moment you heard about a brand. Most of the time brand awareness happens gradually as a form of psychological osmosis. Without this insight, retailers are bound to throw superfluous dollars at a channel that may be better spent elsewhere.

Secondly, the major metric retailers use when it comes to counting leads is store traffic. Campaign is run + traffic increases = winner! But not only the footfall count could be muddled by the "noise" from staff, this figure also means little by itself. It does not separate the buyers from the non-buyers by demographics. It does not show the customer journey. It does not show traffic count at specific areas, especially the ones having the promotion, and how long people browse there. Store traffic alone is inconclusive and oversimplified. Without more in-depth data, online marketers do not have sufficient information to fine-tune their future campaigns.

Store traffic broken down into different segments to provide retailers with more helpful infos.

Last but not least, the traditional appraisal of online marketing effectiveness often comes at the end of the campaign. It is a case of too little too late: by the time the campaign is over, there is not much that could be done to salvage a failed attempt or amplify an unforeseen success. If retailers had a way to closely monitor their marketing effectiveness day by day, week by week, they could make timely tweaks that would ensure bigger ROI. Flexibility is the competitive edge of online marketing over traditional marketing; hence it should be extended to both its execution and its evaluation.

Enters a new development: retailers could now use in-store insights to gauge the strength of their marketing campaign, thus enhancing it. Thanks to the groundbreaking advances in AI and computer vision, in-store analytics softwares could now distinguish the staff from the customers, allowing retailers to have an accurate measure of traffic, especially at the "hot zones" - where products with promotions being run online are located.

A categorization of the levels of engagement at different retail sections.

Within the customer group, the possibilities are truly unlimited. AI softwares could separate the buyers from the non-buyers, then divide them even further into subsets like ages, genders, and locations to see if the online targeted marketing takes hold. One single shopper's profile may not imply much, but as an aggregate, shoppers provide a well of information: their collective tastes, so subtly revealed by where and how long they spend browsing; their conversion rates, broken down into demographics; their behaviors, classified and analyzed throughout their journey in store. Based on the constantly updated data, new key groups for online marketing could be formed or discarded with great efficiency.

Detailed analysis of customers divided by interaction rates across cities and periods of time.

A sensible strategy for online marketers would be to take advantage of A/B testing. Instead of running one big mass campaign, it would be wiser to spend the same budget on multiple small ones, swapping out components to arrive at the most optimal. For example: a footwear retailer could run slightly different Facebook ads in Ho Chi Minh during a week to see what works best for Ho Chi Minh audience, then run that same Facebook ads in Hanoi, Danang, and Ho Chi Minh the next to see which city has the most enthusiastic response. Different channels could be further combined for maximum results. The more customized and well-informed online marketing is, the better chance it has converting customers into buyers.

A simplified demonstration of nationwide testings for ad campaigns.

In the past, retailers have used online data to influence brick-and-mortar stores' business decisions. The process has come full circle: it is now the physical stores' turn to guide the online world. The prospects are not limited to advertising: there are improvements to be had in inventory, customer experience, communication, and product strategy. With AI-powered solution at the helm, it is only a matter of time before retailers could reach omnichannel.



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