Online share is to permanently exceed offline in terms of sales
Instead of being led by educated guesses or outright gazing into the crystal ball, today’s CMOs and marketers need to be digitally driven and base their decisions on contextual analytics. Below, you can find the summary of one of the latest blog posts written by Karel Tlustak, the founder of Business Factory and ROI Hunter, and now also Marketing Intelligence.io, which describes the future of e-retail as discovered by the MI.io’s own contextual analytics system.
The corona crisis certainly is a horrible situation, especially for the retail industry as in many cases, whole supply chains have been paralyzed. To be specific, about 30% of businesses are assumed to be dependent on China, whose full recovery is expected no sooner than around September 2020. Nevertheless, the terrible situation also opened several seriously beneficial opportunities.
As the internet has become the main source of information and human interaction, and therefore traffic has increased, the price of online impressions have already fallen by 30% and keeps decreasing. So it is now a great time to invest in online advertising as it is inexpensive.
E-commerce websites are the most obvious beneficiaries of the outbreak. Customers are now forming new shopping habits. For example, not only have the customers quickly adapted to online shopping but buying online is now also their preferred way of obtaining items which they used to buy strictly only offline before.
We will soon witness one of the greatest shifts in the retail industry. E-retail sales are to outperform offline sales and permanently retain dominance; forecasted share to be 54% by 2023. Simply put, as MIT University research shows, online retail is soon to become the new norm.
As you can see in the forecast above, whether a company invests into online advertising now is to make a huge difference in terms of the company’s future results. Basically, it is necessary to look at one or more steps ahead and use the opportunity to cost-effectively invest in online advertising, which leads to a greater profit than if you made a different decision. Such insight may be gained through the secure aggregation of all company data (e.g. Facebook, Google Analytics, Google ads, CRM, accounting system, etc.) and employing machine learning to predict the results. For example, the solution by Marketing Intelligence.io can deliver the first forecasts already after a few days of running.
In regard to the retail industry, e-retailers now need to fully exploit the current conditions and maximise profits which will simultaneously allow them to generate resources that may be needed for potential challenges throughout the coronavirus aftermath. For that reason, it is now very wise to promote the specific items in your stock that generate the highest profits. No matter if an e-retailer’s offering consists of a few or millions of items, the cost-efficiency and profitability should now be the key factor more than ever.
In fact, the mere availability of a product is now one of the greatest competitive advantages an e-retailer may have right now. Advertising less profitable products and services should now be avoided and the resources should be put rather in identifying and stocking the most profitable goods (products’ ideal cost of advertising VS margin from the product).
Before the coronavirus outbreak, the year-on-year growth of retail was predicted to grow only by 2% and if it was not for the coronavirus, online sales would account for only 15% of the whole retail. But now, one of the greatest transformations of the retail industry is expected instead. As you can see in the graph above, forecasts show an inevitable rise in online sales by tens of percents even after the coronavirus restrictions are loosened.
This insight will help you to fully exploit the coronavirus situation. Not only now, but also in terms of long-term adaptation to the new norm in the retail industry. Should you wish to gain more insights like this, such as the analysis of competitors’ online activities, the best first step for you would be to set up an automated dashboard and connect your very own data warehouse to the dashboard to gain access to easily available and up-to-date information.
Systems like the one from Marketing Intelligence.io can start delivering first results in less than a week after you start applying machine learning to your data. The implementation itself may be costly, but there are ways of minimising the expenses, such as using your own data warehouse and outsourcing data-science instead of hiring an analytical team, or applying for grants which can cover the implementation costs – such as this one from Facebook.