02 Aug 5 Vital Considerations for Big Data Analytics in Retail
5 Vital Considerations for
Big Data Analytics in Retail
Retail businesses collect more data than ever before, and from every aspect of the supply chain, including:
- Logistics data (vendor compliance, lead times, etc.)
- POS data (sales, returns, etc.)
- Inventory levels (store, warehouse, distribution centers)
- Traffic cameras (foot / vehicle traffic)
- Prices (markdowns, promotions, competitor prices, etc.)
- Consumer behavior (average ticket price, product preferences, time, weather, etc.)
- Demand forecasts
- And much, much more
But what good is all this data if you can’t derive real value from it?
Being able to monitor and track real-time data sounds great, but in reality, that is the same as watching money pour out of your supply chain. The entire point of collecting all this data is to have the ability to make smarter decisions going forward; get real value out of it; and to prevent unnecessary costs proactively.
While Business Intelligence tools provide improved visibility into your retail business, these tools don’t provide any actionable recommendations moving forward. Predictive analytics systems, driven by artificial intelligence (AI), let you recognize opportunities, anticipate risks and opportunities, and accurately forecast demand far enough in advance for you to make those smarter decisions from Big Data.
1. Business Intelligence vs. Predictive Analytics & AI
Traditional BI tools only tell you what happened in the past. At best, they give you a dashboard of conditions right now.
These BI tools are descriptive, but they aren’t predictive or prescriptive.
In the past, the solution to the lack of predictive or prescriptive power was simple — you simply unleashed your analysts on the data. They got to do what they love by diving into the underlying data, ferreting out trends, and surfacing insights.
But with the amount of data your business is collecting — this is becoming unfeasible.
Consider the way retailers traditionally forecast fulfillment.
Analysts would pull historical trends from BI tools and make adjustments based on this year’s outlook as well as input from other stakeholders. Add in a final fly-by-your-gut manual tweak and the fulfillment forecast would be ready.
This approach doesn’t work in the era of Big Data because it’s based on a false assumption: past performance is not a true indicator of future results. Your assortment is different, consumer behaviour is different, your store count is different, your competitive environment is different.
Not only that, the historical data does not tell you anything about the demand you missed due to pricing, cannibalization, or stock outs.
You cannot understand true demand without analyzing hundreds of variables. Without that understanding, your fulfillment forecast may leave shelves empty or your stores floating in excess inventory.
Traditional BI tools can’t provide those insights.
Moreover, you can’t hire enough analysts to forecast true demand for every SKU in every store, much less answer questions like:
- How do I forecast demand for a new product with no sales history?
- How will a promotion during a seasonal peak affect the uplift in demand?
- How deep should my assortment be in a particular category?
Predictive analytics tools use the power of AI to do this heavy lifting. They create more accurate, more reliable measures of true demand so your analysts can help you make more forward-looking decisions.
2. Unifying Big Data across Forecasting, Planning, and Operations
Predictive analytics systems go beyond traditional forecasting by letting you unify processes within your business.
In the past, forecasting and analytics happened within organizational black boxes. Each functional team looked at data differently, had different priorities, and had different decision-making lead times.
Because retailers can’t afford to lose money because inventory purchases didn’t account for future promotions, business leaders imposed processes to keep everyone on the same page. The result? Analysts generated more reports than ever. Merchants and inventory managers attended more co-planning meetings. Additional reviews yielded manual corrections to reconcile each team’s work.
All of this effort consumed ever-more resources and slowed decision-making.
Today’s business environment is too dynamic for that approach.
Your teams must be completely unified around a common end-to-end process so they can collaborate more effectively. A predictive analytics system provides that unification by producing a shared, accurate, and consistent understanding of true demand.
3. Business-Specific Predictive Analytics
This promise of AI-driven predictive analytics is powerful, but retailers must resist the temptation offered by enterprise software vendors and data analytics firms.
There is no general AI solution that works universally across industries — or even across the retail industry.
The unique data challenges that jewelers face are very different from those faced by pet supply businesses. Retailers in Vancouver operate under different conditions from retailers in Houston or Dubai.
Dumping your data into a tool that hasn’t been fine-tuned to your unique retail business is like shaking a box of puzzle pieces and hoping they’ll fall out in the right order.
Their analytical engines simply won’t produce the forecasts you need.
This is largely in part to the fact that they are not business-specific, meaning they will not account for specific business constraints, preferences, or exceptions that can make all the difference at the end of the day.
Out of the box solutions won’t take any special considerations into account, like:
- Size curves for fashion retailers
- Seasonality for specialty gift products
- Regional store differences
- Unique supply chains
- Personalized products
- Product launches with no sales history
So in order to use these enterprise analytics platforms, you’ll have to develop a wide range of customizations, adjustments, and layered exceptions rules to get a true picture of your business.
Training effective AI models must happen at a granularity that matches your unique business rules, processes and market conditions. This is the only way for the AI or predictive analytics engine to account for variations in store type, local seasonality, customer behavior, and hundreds of other factors that influence true demand.
4. In-House Development vs. Proven Solutions
Tailoring an AI system to be unique to your business does not mean that you should develop it in-house.
Terms like “Artificial Intelligence” and “Machine Learning” and “Predictive Analytics” have become common buzzwords in the business press. However, these cutting-edge computer science skills are nowhere near that common.
Your team understands your customers, how to be effective merchants, and how to manage your supply chains. Nothing that makes you a successful retailer has prepared your company to develop AI systems.
So trying to do it in-house is fraught with risk.
You could easily spend millions of dollars over the next few years only to learn that your system will never work.
A big reason for that failure will be turnover on your IT team. Advanced AI skills are so rare that the recruiting environment is fierce. In a seller’s market, your developers will leave for greener pastures long before they complete the project, or worse, taking all the unique knowledge and training you’ve invested with them.
Partnering with a team that is as experienced and passionate about predictive analytics as you are about retailing is the better option. Your team can focus on retail excellence while tapping into your software vendor’s deep technical expertise as well as strong experience working with successful and progressive retailers. Rather than perpetually maintaining an in-house project, your partner’s world-class retail technology will scale with your existing systems.
5. Choosing a Partner with a Proven Track Record
How do you choose the right partner?
Plenty of companies wrap the AI buzzword around the same claims. Some are dressing up older technology in more modern clothes.
Others are pitching unproven solutions in search for retailers willing to be their guinea pigs.
Retailers need a partner that combines deep AI expertise with an established history in retail management technology. The right partner will have a proven track record delivering truly unified predictive analytics and AI solutions that are truly business specific.
Retalon can be that partner. Our customer stories will show you how businesses across retail verticals generated better forecasts and made better decisions that led to tangible ROI. Ask for a personalized demo of Retalon’s end-to-end predictive analytics platform. We can even run your own data through our system. Know the ROI before you buy! A quick Proof of Concept will show how Retalon can help you turn Big Data into smarter, forward-looking retail decisions.