With the help of new retail RFID La technologie, intelligence artificielle et autres technologies. Les détaillants peuvent prendre le contrôle des données commerciales et les appliquer. To ensure customer satisfaction and continue to maintain an advantage over competitors.
Supply chain disruptions
Shifts in customer behavior, and even unexpected weather events are impacting both e-commerce and brick-and-mortar retail sales. Changing where, quand, how and why customers buy. It is the retailers who can respond most quickly and accurately to these volatile market forces and behaviors. That are creating the most innovative and positive customer experiences. The rest are at risk of losing customer loyalty, brand affinity and revenue.
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A Recent Study
Found that 83% of retailers say they are under-utilizing customer data. This is problematic. Because customer data should guide most business decisions, including marketing, gestion de l'inventaire, merchandising, et plus. A lot of early data is not useful. This data is difficult to adapt to changing conditions and predict customer behavior.
The Power of Data
So how can retailers regain the power of data? Human analysis alone is no longer the answer. There is too much data to analyze, and a lot of it changes too frequently. It’s time to adopt AI-driven automated data analysis tools to handle today’s massive amounts of data. AI analytics can sift through all of a retailer’s data on a daily basis. No matter how many data sources there are, and as soon as it finds an unexpected change. It will be immediately brought to their attention. This enables analysts and business leaders to quickly and easily uncover risks and opportunities hidden in millions of retail data points.
Sorting out the artificial intelligence landscape
There are many AI data analysis tools that claim to find the answer. When choosing the right tool for the job. It’s important to look for key features and capabilities that will add value to analyst teams and other organizational leaders. Who need access to AI’s salient insights.
Minimum implementation requirements: Adding another platform to your tech stack can take months of setup and ongoing maintenance. Which often limits its flexibility and can take longer than expected to provide useful insights. Instead, look for a SaaS solution that sits on top of existing data and reporting platforms. And doesn’t require lengthy implementations or custom integrations built just to access existing data stores. A free trial is also always a bonus.
Proper Integration with Key Datasets
AI works best when it is well-integrated with data from key business data sources. Identify a solution that complements existing analytics and BI tools and leverages data from leading platforms. Including Google Analytics, Facebook and other social channels, Adobe Analytics, Snowflake, SAP/HANA, MySQL, et plus. Ideally, the platform offers zero-work integration, meaning new sources can be connected in minutes instead of days. And you can add new data connections as needed.
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Daily Reporting of Actionable Changes in Data
A common misconception about traditional BI dashboards is that they detect changes in data and behavior that quickly lead to action. But because they’re built to answer questions or scenarios you’ve programmed in the platform. BI tools ignore changes that show you didn’t know to ask, were unexpected, or unknown. Ideally, an AI platform continuously monitors all data to highlight changes that brands and analysts don’t want. Rather than just building more dashboards. Look for an AI platform that automatically discovers and proactively emails your team to changes on the platform every day. To ensure more immediate and targeted action.
Provide reports for every team member
Most data reporting tools on the market offer customizable dashboards. But what you really want is a platform that considers both business leaders and data analysts. The solution should provide data stories that are simple enough to be immediately understood by non-technical business users. While also enabling analysts to drill down to the details of root cause analysis and comparisons as needed.
Identify the Right use Case
With automated smart tools, retailers can leverage all customer data to uncover emerging customer experience issues or new growth opportunities. From store layout and merchandising to digital experiences and social media. Retailers can leverage changes in customer behavior data to understand what translates into increased revenue and brand loyalty. While uncovering new trends, areas of opportunity and hidden relationships.
So where to start?
One approach is to identify new unresolved problems in the business or changes in customer behavior that have no apparent root cause. Another is to look at other retailers’ use cases. To see how AI can reveal unknowns that can improve revenue or customer experience.
In one example
Marketers for a leading bath and beauty brand were alerted to unexpected increases in product category sales when overall revenue was generally declining. With an automated business analytics platform, bath and beauty marketing teams are automatically notified when candle sales exceed expected sales.
Expected Sales Performance
The team didn’t analyze every one of their thousands of SKUs based on expected sales performance. Because no analytics team could manually analyze too much data on a regular basis. But AI tools automatically uncover this insight, and in doing so help marketing teams steer toward specific product trends so they can bring in additional revenue. le “sprouting” of this opportunity is a great example of how the next great marketing strategy can be hidden in obvious business data. But can’t be found without help.
As a Result
bath and beauty brands were able to quickly launch marketing campaigns to promote candles and capitalize. On this positive change in customer buying behavior. This unexpected insight also helps the team ensure that inventory levels can be aligned with the new expected sales. By simply spotting a trend, the brand is able to generate more sales by tapping into otherwise unseen potential revenue streams.
In another example
A CPG company is managing a warehouse with hundreds of employees receiving and shipping perishable food. Using automated business analytics. They found quarterly low metrics for the amount of time it took to initiate and complete tasks in a particular warehouse queue. Working hours at this stage were significantly shorter than average. And the CPG company wanted to figure out how to replicate this improved process in other cohorts. To increase workflow and operational efficiency.
Rapidly Integrating
By rapidly integrating existing data into its AI tools, the brand identified active queuing activity among specific employees. The company then identified the different practices of these workers and used those learnings and practices throughout the warehouse. Par conséquent, the brand was able to increase overall production and sales that would otherwise go unnoticed . A missed opportunity to improve the overall retail business.
Price water house Coopers said
To stay competitive and keep up with changes in customer behavior. More than half of companies are looking to incorporate artificial intelligence into their digital strategies. By identifying how AI can benefit the business and leveraging tools that can be rapidly deployed and integrated, retailers can take control of their business data to ensure they keep customers happy and stay ahead of the competition.
Mike Stone is Chief Marketing Officer at Outlier. Artificial intelligence, responsible for the company’s market growth strategy. Demand generation, communications, product marketing and inside sales. For more than 20 années, Stone has led marketing organizations and provided strategic consulting to technology companies. Most recently, he served as Senior Vice President of Marketing for mobile customer engagement provider artificial intelligence reship. Before that, Stone led the marketing efforts for Salesforce Community Cloud, from its initial launch to four years of rapid global grow.
