said Tanya Long, CEO of Argility Technology Group.
Articles published in the past ITWeb has reviewed several challenges and solutions that retailers should consider to limit the impact of load shedding on their operations. At Industry Insights, we looked at how partnering with trusted suppliers can leverage enhanced technology to solve this problem, but there's more you can do.
There are fundamental changes in production, supply chains and consumer behavior that cannot be ignored. This is where we rely on the technological innovations of this century, including artificial intelligence (AI), machine learning, predictive analytics, and cloud solutions.
To be sure, predictive analytics is not a crystal ball. But we can't ignore the enormous power it gives retailers and brands.
To use a casino analogy, let's say you play roulette. In the old days, I would look at the tables and assess each dealer's tendencies to decide which table to take her R100 chance at. Imagine someone whispering to you – “Table 1: The probability of red is 65% and the probability of black is 35%”. What decision would you have made? And if this kind of insight was provided to you every 15 minutes, were you more likely to include it in your next action decision?
Predictive analytics is not a crystal ball. But we can't ignore the enormous power it gives retailers and brands.
This is the benefit that predictive analytics can provide. In short, it's the power of data-driven decision-making.
Machine learning allows us to continuously use fluctuating behavior to understand supply and demand patterns at a rate that is incomprehensible compared to five or 10 years ago. The traditional software approach has been to create system rules based on known rules. However, as situations become more complex, using machine learning to identify patterns and provide deeper insights becomes an intelligent approach.
It is often said that the advantage of AI is that it never sleeps, works 24/7, and has exactly the capabilities needed to help us in today's economy.
Retailers and brands can now glean insights faster from multiple inputs about availability, pricing, placement, and customer demand.
Predictive analytics uses the principle of small compound profits. for example:
- What is the positive impact of a 2% increase in inventory inventory?
- What is the effect of a 2% increase in cash flow due to a decrease in superannuated stock surplus?
- What would a 1% increase in gross profit mean if you were able to quickly and strategically manage your stock price by taking into account the pricing appetite of your entire product basket and the pricing of your competitors?
Now, we know that all of this requires data, but the general discussion in the media has raised the question of how much data we need. When should I start using analytics? The general consensus is that if you haven't started yet, you're already behind the curve.
The advent of the cloud has made it possible to cost-effectively store and process petabytes of data without excessive hardware investments. You can also easily scale as your needs grow and change, leveraging the always-on infrastructure provided by global companies.
What problems can predictive analytics solve?
Let's start with the concept of demand forecasting. In the past, inventory and sales forecasts were typically based on moving averages. It would be unwise for today's retailers to continue in the same way. Predictive analytics can add immense value to this field.
Another area where predictive analytics can play a big role for retailers is by focusing on customer patterns. Not only can you personalize by putting the right product in front of the right customer, at the right time, and at the right price, but you can also measure changes in shopping behavior.
Using machine learning for clustering and segmentation to identify purchasing habits and preferred products streamlines personalized recommendation strategies.
In a credit retail environment, additional insights into areas such as purchasing trends and customer lifetime value can improve lead generation efforts. There is no doubt that anticipating customer needs can not only help retailers build customer loyalty, but also reduce the cost of spray-and-pray marketing efforts.
Labor management is another area where data and analytics can play a key role in improving operational efficiency. One cost pressure that retailers can control is considering optimal employee levels.
By collecting data on the speed and accuracy of staff performance, retailers and brands can streamline operations and respond efficiently to fluctuating business demands.
The use case does not end there. We recommend starting in areas where you have a compelling business challenge and can measure and adjust results to take appropriate action.
While business and project sponsor support is critical to success, I think many stakeholders would rather be proactive and stack the odds in their favor.
