Using Data to Inform Pricing & Promotion Decisions [a Q&A with Rubikloud]
Leveraging data from disparate sources to provide more personalized promotions to consumers can seem like a fool's errand. The efforts, however, can be rewarded with higher key performance indicators (e.g., click throughs, conversions, average order values, loyalty).
As part of our, "4 Companies You Didn't Know Worked in Artificial Intelligence" feature, Website Magazine caught up with Rubikloud CEO Kerry Liu to learn more about how his company helps retailers gather data from consumer behaviors and use it in meaningful ways.
What are some common problems of retail promotions?
Retailers tend to struggle with gaining a holistic view of their customers due to outdated legacy solutions. Oftentimes the online and offline interactions become siloed due to disparate systems, and because of this, retailers aren’t able to deliver the most accurate insights when creating promotions and market forecasts.
How does Rubikloud learn from a retailer’s promotions and what’s the result of that analysis?
Rubikloud uses their two machine learning tools (Customer LifeCycle Manager and Promotion Manager) to gather data from retailers and their customers from online and offline touchpoints. Rubikloud’s cloud-based architecture allows for retailers to derive fast and accurate insights that can be used to inform pricing decisions, and personalize coupons and product offerings to customers. As data is fed into the cloud core of the Rubikloud product, the two machine learning tools are able to pull out various insights and turn them into highly curated offerings for individual consumers. This results in greater customer loyalty and conversions as retailers are able to learn from customer behavior, and predict and plan for it. This also saves retailers time and money as these promotions optimize resources, and retailer decisions and marketing campaigns are empowered through more accurate trend forecasts.
Help me understand where the retailer’s promotional data is mined from (e-commerce platform, content/Web experience management solution, etc.?) and then how it gets used by Rubikloud.
The Rubikloud products integrate with the execution layer of existing marketing tools, merchandising and email software. Once plugged in, the machine learning algorithms explore and analyze the online and offline customer behavior that shoppers exhibit throughout their buyer journey and then are able to provide insights about the preference of their customers, especially for those who use the retailer’s services more regularly. This machine learning technology is also used to tap into these pools of data, and forecast pricing and promotion impact on certain products based on market fluctuations and historical data that has been accrued in their systems.
How can Customer LifeCycle Manager help prevent customer churn for non-subscription-based retailers?
Customer LifeCycle Manager helps prevent customer turnover by providing a hyper-personalized experience for consumers. This tool ties directly into a retailer’s system and pulls historical and real-time data to provide the most up-to-date offerings. This enables retailers to supply highly curated experiences based off of data gathered from online and offline customer touch points that facilitate growth in certain product categories. The machine learning tool effectively moves customers up the value chain, generating greater customer loyalty, by providing offerings and promotions that they need, when they need them, based off of patterns gathered from machine learning tech.
Anything else you’d like to add?
It’s safe to say that in today’s digital age data analytics and machine learning are going to be the biggest contributors in improving merchandising, profit margin and customer experience for retailers. International retail players like A.S Watson Group are choosing to migrate 100 percent of their retail data to the cloud in order to move merchandising and loyalty onto a single infrastructure, and they’re turning to big data solutions like Rubikloud. Our big data architecture will help to accurately quantify P&L and cost-savings impact, effectively improving forecasting accuracy and loyalty. It’s now becoming necessary for retailers to make an investment in machine learning and big data, or risk being left behind.