Digital retail marketers face a rapidly changing landscape shaped by the emergence of data as a currency of the industry’s future. As the global economy is digitized, shoppers are leaving an exponentially-growing treasure trove of data in their wake.
Astonishingly, the vast majority of the world’s data since the dawn of human history has been created in the past two years alone. Unlike traditional currency, this resource is abundant and seemingly limitless, but it can be difficult to use correctly. Yet it can also be lucrative in its own right. US retail media is growing by over 10 billion USD per year as vendors invest in an ever-widening array of retail media platforms and features. Much of the rest of the world is following suit.
The rise of ecommerce has meant that every detail of shopper demographics, the customer purchase journey, and product inventory and content quality can increasingly be tracked. Furthermore, subscription and loyalty programs mean that digital marketers can increasingly track in-store individual behavior over time. Broader digitization also means brands can better track internal processes. To make the most of available data, digital marketers must pursue a holistic Retail Ecommerce Management (REM) strategy. They must master the three A’s:
Before brands can make intelligent decisions, they must aggregate all their available and relevant data. For instance, ad spend should be tracked across all customer and retailer accounts to enable easy, omnichannel comparisons. Furthermore, internal data should not be locked away in different informational silos that rarely speak to each other.
Instead, inventory should be an input into marketing decisions. Not integrating this data risks costly mistakes and lost opportunities. Having a single source of truth to house and present all of this data is imperative. Shopper, brand, retailer, and backend supply chain data should all be accessible through a single platform capable of quickly aggregating and trending whatever data a digital marketer wishes to understand.
Second, data needs to be continuously analyzed to better understand the market. For larger retail brands, human-based approaches are too time-consuming and incomplete. There is simply too much data to analyze. Enter machine learning and AI. Using preset parameters, algorithmic retail ecommerce can surface relevant trends and opportunities that employees might never uncover on their own. A savvy marketer cannot know every detail about their consumer, but they should have access to data and tools that can answer any question they need to answer.
The final step is taking action. All the data in the world is useless unless it can be used. But as data has proliferated, so has the number of potential decisions. For larger brands, hundreds of micro-decisions need to be made and executed every day to stay ahead of the market in real-time. Human-based effort and processes cannot possibly keep up with the actionable steps that so much data and analysis demands. In a digital environment, AI and machine learning must be used to effectively manage a retail ecommerce strategy.
These tools should be based upon preset parameters that align with a larger strategy or goal. Dynamic pricing, advertising spending adjustments, and routine processes like error detection and filing tickets should all happen automatically without any needed direction from a human person. Once these decisions are automated, the marketing team is freed to focus on what people do best: establish and maintain the bigger vision that algorithms can then automate.
How can brands take steps to leverage more data and machine learning in their retail ecommerce strategy? Here are five actions that digital marketers can take right now:
Review current data pain points and software service providers: Look for opportunities to aggregate and combine functionality under one platform, then eliminate redundant services.
Invest in the right tech partners: If needed, survey the market and determine who is best positioned to handle your data and align with your goals as an organization. Aim to automate routine tasks as much as possible to gain incremental revenue.
Determine common KPIs to minimize disagreements. Many brands spend too much time on internal disagreements that stem from analyzing the business using different metrics. For instance, should you use ROAS vs. Share of Voice when evaluating marketing performance? Decide on common metrics that all parties can respect to move the business forward.
Provide clear lines of communication internally. Ensure that key stakeholders across different divisions of the organization can be heard regarding their metrics, data, and goals. Communication must be continually encouraged.
Monitor the market. Look at how the market and competition are reacting to your strategy and respond appropriately with both small tweaks and big overhauls for each campaign. Automations and machine learning are only as effective as the rules that guide them, and they will need to be updated regularly to align with the market.
(Views expressed are personal.)