How Big Data Influences Retail Success
Big Data has been both loudly and quietly changing the retail landscape. Old models are presented with new challenges with which they cannot keep pace without making changes, and new models are rapidly developing to both push e-commerce forward and streamline the face-to-face retail experience for consumers. For those who take advantage of these new ideas, gathering and utilizing the data, there are a slew of benefits to reap.
First, you need to know what big data is.
Big data refers to a large volume of data that is both unstructured and structured. It’s the small pieces of information that flood a business on a daily basis. It’s also information that is so large in volume that it more often than not demands specific software to be used to process that information is any sense can be made of it.
It’s not the information itself that is most valuable, however, but how you use it once understood. It doesn’t matter how good the baseball bat is if the player doesn’t have a good eye and a steady swing.
Changes Being Made by Big Data
More Efficient Logistics
Logistics touches almost every aspect of retail. For online sales, it involves the shipping of products from manufacturers to warehouses, shipping from warehouses to consumers, and return shipping from consumers when appropriate. During all of these processes, logistics also accounts for the tracking and documenting of the efficiency of those processes. From this, the aggregate data can be taken to find weaknesses in the supply chain later on.
For face-to-face retail at brick-and-mortar locations, logistics entails much of the same. Orders are placed to bring in products to sell, then they are shipped, stored, tracked and documented along every step of the process.
In both cases, the aggregate data can show where the supply chain is at its slowest, or which part is most prone to complications. Once these bottleneck and/or chokepoints are discovered in the supply chain, they can be studied and brought up to speed to improve the efficiency of the process at large.
With information on consumer response to price made available instantaneously across the world thanks to big data solutions, it doesn’t take long to see whether there is a positive or negative affect to the bottom line when the price of a product is dropped by a dollar, or increased by a dollar. Knowing the percentage of sales lost or sales gained in correlation to a new price allows for incredibly efficient and timely tweaking to maximize the revenue of any given product.
Putting this in perspective, if you can raise the price of a company’s services by just 1%, the company’s operating profits can increase as much as 12.5%. Knowing the threshold of what your consumers are comfortable with is the difference from increasing profit by over 10%, or a loss in profit from the status quo.
Understanding and Capitalizing on Shopping Behavior
Similarly to how pricing strategies can be created and implemented much more quickly by measuring the response from consumers, the same can be said of brick-and-mortar store layouts and stock.
This requires identifying the patterns found in a given store’s customers. Once this information is gathered it can be put into action through updating POS (point of sale) systems as customer practices demand or indicate demand. If the 10-item-or-less checkout aisle is always backed up with customers buying one or two items, then add self checkout stations, for example. If a certain item is going out of stock an average of 9 days after stocking it, then additional “surplus” is needed to prevent selling out without having to hold onto extra inventory for very long. It’s a balance.
Customized Customer Service
If you have an account with Amazon, Netflix, or Spotify, you know that there are regular suggestions presented to you on their websites and applications which are based upon your preferences. They take what you show interest in and provide suggestions for similarly themed products. Not only does this customized product placement serve to make their platform more useful for you, but it can work to provide customized customer outreach and customer service.
Newsletters can be customized by groups of customers. Say you have a group of customers who are recorded as spending 60% of their spending budget on one type of product or service that you provide. You can write a letter specific to their displayed wants for a special offer coming up, and send it just to those relevant customers.
Knowing what will be in demand tomorrow is at the level of practical magic in terms of utility. Being able to predict what retail trends will be hot ahead of the curve will put whoever has that knowledge a good three steps ahead of the competition.
Trend forecasting applies algorithms to social media posts and web browsing habits to figure out what is creating a stir today, and that can easily translate into what will sell tomorrow.
Having a qualified team practice sentiment analysis is what will make sense of the flood of data that is encountered trying to make heads or tails of it all. Sentiment analysis is the process of using machine learning-based algorithms to process and determine the context a product is being discussed in. That will show whether it is in a good or bad light, and whether or not it is a mild curiosity, or an outpouring of praise.
Beyond the Now
Predictive analytics that identify what to stock before it becomes a consumer craze, personalized customer service and outreach, customized design of floor plans in response to customer behavior, sharper metrics on how much of an item to purchase and when, improved logistics and supply chain management, and hyper-streamlined pricing strategies… These are only a few of the present effects on the retail environment today that have resulted from the influx of big data and big data analytics.
The landscape is changing faster than ever, and all in response to customer demand. Whoever most effectively and most quickly can keep the pace to providing the best customer service will rise to the top – just look at Amazon’s success as an example.
Whether brick-and-mortar, e-commerce, or a hybrid of the two, there is room to expand and improve functionality using big data. The particular nature of how it will be more realized in the future cannot be definitively stated, but the results in profit for those using it already is a certainty.