Data crunching company lists 5 ways retailers can compete with Amazon
Local retailers can compete with the Internet giant Amazon and here are five ways to just that according to Dextro Analytics.
The global retailer sees sales rise every year. Trying to match Amazon is a sure recipe for failure, says Manmit Shrimali, co-owner of Dextro Analytics. Amazon offers price points and a sheer variety of goods local retailers cannot match. So what retailers need to do is shift the focus away from being a giant store and concentrate on what local retailers have always excelled at.
1) Personal attention. Local stores employ experts. These experts offer face-to-face interaction with customers. They can see when a customer is having problems and step in to help. They can help guide a customer to the proper choice. A lot of companies offer loyalty cards that give a customer a price break on purchases. Amazon does something similar with the Amazon Prime account, but that costs money. A loyalty card should not cost anything.
If a company doesn’t have a loyalty card, make one. Business cards are cheap. Order cards and have a hole punch for notching the card. When the customer reaches a certain number of notches or holes, they get a bonus. This works very well in the restaurant trade and it can translate directly into retail.
Businesses should be collecting consumer information at the point of sale. Get emails and phone numbers with the name. Use this to send messages, such as birthday greetings, holiday greetings and special sales on items the customer has purchased in the past. “You have a wealth of information in your point-of-sale system. Start using it. Show your customers that you care about them and they will care about and shop with you,” said Ajith Govind, co-owner of Dextro Analytics.
2) Plan sales carefully. The mantra of shopping malls and giant big box stores is something has to be on sale all the time. That effectively means nothing is on sale, because the sale price is normal. How can a retailer get around this mindset? Precision. Shrimali again points to the wealth of consumer information stored in a retailer’s computer system. “You have the data showing when people will buy certain products. You have the information telling you what products sell at certain times of the year. You just have to extract this from the files of big data stored in the system,” he said. Artificial intelligence–based algorithms can match SKU performance to buying habits and this information can be paired with inventory to maximize sales and minimize long-term storage of product that only moves at certain times of the year.
“You can also match expected sales to the inventory so you don’t over-buy from a wholesaler. Having a bit left over at the end of the season is not bad. Having pallets full of product with an expiration date and the season is over is a big problem. Too many times, retailers have to sell this stuff at cost just to get it out of the store and avoid losing even more money,” Govind said.
3) Make sure the supply chain is lean. In the manufacturing world, this is called “just in time.” A manufacturer makes something, ships it and it arrives at the destination just in time to be used. The manufacturing plant is not sitting on huge amounts of inventory waiting for buyers. Here are some ways to adapt this thinking to retail:
• Watch the supply chain. Stock the right items and get that product the by the best delivery method. Watch the price for shipping as well as delivery time. Modern algorithms can take big data from the supply chain and deliver quantifiable information that can be put into action. Results can be as soon as immediate to however long it takes to order and get a new product shipped in.
• Watch returns. Returned items, especially Returned Goods Damaged (or Defective), are a major headache for the retail industry. Some RGD can be repackaged and sold at a discount, some cannot. Inventory tracking and returns information has all the data needed to know what goods come back most often. “Through Markov-based time series analytics, we have been able to help retailers predict with high precision which SKU is most likely to get returned. With such precision, retailers can reduce the returns by as much as 18%,” Govind said. Items that have a very low return rate can be put on sale while high-return items can be priced to reflect the cost of RGD. Digging this information out of the total volume of sales may require checking records over several years. Modern analytics makes this feasible.
• Dynamic segmentation. Get personal with customers with email. As noted, a good point-of-sale system is going to gather personal information about the customer base. This big data can be refined down to the individual level. Pair that micro-level information with an automated personalized email marketing program and retailers can make the kind of connection to customers that has never been possible until now. It shows a personal touch. “The big data must be analyzed and reduced. Extraneous information must be filtered out and what’s left reduced to manageable files. The most effective sorting will cover a customer’s buying habits over several years,” Shrimali said.
4) Get predictive. As people get older, their buying habits will change too. By examining past spending habits, future buying can be predicted if the retailer pays attention to shopping trends. Current spending also will indicate what consumers are likely to buy in addition to what they are looking for. This is where Amazon excels. A shopper looks for something on the giant retailer’s website and Amazon adds recommendations.
“Today’s websites, even for the smallest retailer, need to do this. This is real-time data and has to be presented immediately. A website has one chance and only a few seconds to grab a customer’s attention and hold it. Part of holding that person and turning them into a buyer means giving relevant choices,” Govind said. “That information is available. The analytics looks at other shoppers and what they bought. This can provide a predictive suggestion to future customers looking for similar items.”
5) More than big data. Having gigabytes of information is useless unless that information can be distilled into actionable plans. Modern analytical systems can take the physical equivalent of filing cabinets of paperwork, sort them in a few seconds and provide a single sheet of paper with a few suggestions that can be implemented. The suggestions are all based on historic customer behavior. Some examples:
• Price points. Listing the price in odd numbers generates higher sales than a price with even numbers. Example $2.99 is only a penny less than $3, but the customer focuses most heavily on that 2 v. 3.
• Show options. Give a customer choices in sizes, colors and even other products in a similar line. Variety attracts customers.
For more information about Dextro Analytics and how it helps companies boost the bottom line and make real-time marketing decisions, visit http://dextroanalytics.com.
ABOUT DEXTRO ANALYTICS – Data is the new oil, but it needs to be refined to reap its benefits. A vast amount of data is being generated every day, but organizations are still struggling to make more complex decisions faster and more scientifically. We founded Dextro to simplify a client’s journey to decision-making. Dextro Analytics is packed full of curious folks who integrate novel analytics, domain knowledge, and technology to help companies make better evidence-based marketing and operational decisions.
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