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Using Intelligent Automation in Retail

As retail companies invest more in their analytical processes, most are eager to find ways to maximize returns on this potentially large investment. At early levels of analytical maturity, this would include embedding the analytics in existing reports or dashboards, but companies can gain even more by leveraging analytics to bypass manual processes that bog down their employees. Our analytics team calls itself the Insights to Actions team because data doesn’t create value on its own until some action is taken. By embedding analytics into actions through automating tasks and making suggestions for next steps, intelligent automation can directly create value.

What is Intelligent Automation? (And How Does Analytics Make It Better?)

Automation has been around for decades traditionally playing roles in manufacturing lines. As of late, it’s been receiving a new life within more traditionally manual tasks like entering data from a spreadsheet into a CRM system through robotic process automation (RPA). RPA can manipulate and read screens as a typical user would, rapidly completing tasks that would traditionally require large amounts of manual work. Once these processes are mapped, automation bots are developed that can quickly complete tasks that could take users hours or days to complete.

Leveraging investments in analytics can improve these automations even more. Machine learning models can improve the decision-making in automation further reducing the amount of manual intervention in traditional RPA scenarios and expanding the possible scope for what can be automated.

Intelligent automation is not meant to replace human jobs. It can be used as a tool to free up time for your employees to do more high-value decision work and projects. Most automations will require some supervision and monitoring work like exception handling and tweaking parameters.

Why Does it Matter for Retailers?
Like analytics, automation can morph into a buzzword that is thrown out as the solution for all a company’s challenges and obstacles. To better contextualize the power of pairing analytics and automation, we’ve outlined 4 key areas that intelligent automation can reduce unnecessary manual work and improve outcomes for your customers.

Often the first thing a potential customer engages with, retail marketing is a crucial focus to initiate the customer journey and turn interest into purchases. Individualized customer messaging has been shown to improve conversions, but it can be challenging for a retailer to develop that targeted messaging. Intelligent automation helps here by automating marketing and discount efforts based on similar customer behavior. Using analytical techniques like clustering and market basket analysis, you can build a profile of similar customers based on purchase patterns, regional, and demographic data. Your automation can then send targeted advertising based on similar customers, increasing the likelihood of purchase.

For marketing, intelligent automation can enhance traditional rule-based customer segmentation and mass marketing by embedding smart segmentation, targeting, and customized messaging to increase engagement. These actions can build a more personal relationship with each customer and increase their lifetime value.

Customer Service
Customer service is one of the main ways consumers can interact with your company directly. Adding automation and analytics can assist with response times and improve the customers’ experience. Growth in natural language processing and text analytics in the last few years gives models unprecedented ability to understand a customer’s message for the topic, key information elements, and tone. Providing automated responses to simple questions such as “What is my order status?” can free up customer service representatives to handle more complex or intricate customer issues. Emails can be categorized by their content and routed to the appropriate representative for resolution.

Intelligent automations can be expanded to all aspects of customer communication including chatbots, email responses, automated phone systems, and social media messaging. These models can also include other data points such as lifetime value to enable possible appeasements and offers to ensure your top customers keep coming back purchase after purchase.

Supply Chain and Merchandising
Automation can make an impact in your supply chain not just by automating warehouse processes, but also by reducing manual work and decision making around allocation, planning, and price. These three key elements impact each other in various ways and machine learning can model these complex relationships. These models can describe the impact of each lever based on the choice for others. For example, if the merchandising team plans on introducing a new product at a lower price point than similar products in that category, models can forecast the required inventory and automatically generate the specific allocation plans to ensure your stores and distribution centers do not run out of inventory for this new item. This can be done on a global scale, making localized decisions on allocation and replenishment based on purchasing patterns for similar items or historical trends.

Intelligent automation can be leveraged to rapidly adjust parameters and power decisions in the constantly changing supply chain and merchandising world, freeing up team members to seek out new products and source products more ethically.

Fraud Detection
Fraud and loss prevention are often low priority processes at retail companies, but automation can tackle these issues to prevent future loss without significant resources. Outlier models can recognize possible cases of fraud within stores, and combining those with automation can increase operational efficiency by:

  1. Identifying possible fraud,
  2. Initiating workflows to freeze transactions,
  3. Notifying appropriate teams through workflows, allowing them to review and release or confirm fraudulent activity.

Intelligent automation can improve response times for corrective actions as well as prevent future issues through alerting or other systemic changes to limit the possibility of additional fraud.

With the growth of eCommerce and omnichannel retail, returns fraud is a growing problem for most retailers. Whether it is customers “wardrobing” apparel items or returning items that violate policy, retailers must develop strategies to mitigate these losses while not impacting most customers. Machine learning and automation can improve these processes by flagging repeat offenders for returns and interceding before the customer can request a return. These technologies can assist with reaching out to these customers to ensure they understand existing policies and offer alternate routes to prevent frequent returns.

Getting Started with Intelligent Automation

The benefits of automation are clear, but the steps to get to true intelligent automation can sometimes seem insurmountable. To start, you must have a clear idea of what processes look like in the organization. Are there processes that routinely take a lot of time? Are there processes that require a large amount of manual work to complete? Once you have those, you can map out each of these processes and prioritize key areas that could benefit from automation, whether simple task automation or entire analytical process redesign. Once this knowledge is in place, teams can develop the automation tools and analytical models that can increase the efficiency of your existing workforce. Any models used here must be thoroughly tested, validated, and monitored to ensure there are no unexpected impacts on the business. It’s important to keep in mind that model drift is normal – data changes naturally over time and modeling requires adjustments. Even once productionalized, these bots and models must be regularly maintained and monitored for any sort of drift from expected results.

Companies that smartly incorporate these sorts of tools and automate processes will sooner realize the value of data and gain an advantage within the hyper-competitive retail space.

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Contributions by Brandon Regnerus

Tags: Analytics, Data & Analytics, Retail