Tag Archives: retail

Predictive Analytics helps Retailers to make sense of Big Data

“The most successful retail companies are utilizing data science and predictive analytics (PA) to improve efficiency, improve marketing campaigns, and gain significant customer insight for a competitive advantage” says Christine Kern, contributing for Innovative Retail Technology. But what about the “not so successful” retailers? How can they share in the advantages that Big Data and PA offer? Retailers can – by using predictive analytics.

What is Predictive Analytics?

Predictive analytics is a set of business intelligence technologies that uncovers relationships and patterns within large volumes of data that can be used to predict behaviour and events, according to Eckerson (2007) 1. Or, as Eckerson states it more bluntly “Predictive Analytics is like an “intelligent” robot that rummages through all your data until it finds something interesting to show you.”

Also, forecasting is about predicting the future, and predictive analytics adds questions regarding what would have happened in the past, given different conditions. Therefore, PA attempts to quickly and inexpensively approximate relationships between variables while still using deductive mathematical methods to draw conclusions 2.

Gregg Brunnick, Director of Product Management & Technical Services, Business Systems Division, Epson America explains the usefulness of PA: “If you know how many cheeseburgers John sold during last Tuesday’s lunch hour, for instance, you can improve the efficiency of your food ordering, preparation, labor, and marketing operations.”

The value of Predictive Analytics for retailers

Deon Abott of Smarter HQ writing in Inside Big Data, suggests that data science and predictive modeling have become the holy grail for the retail industry. For this reason retailers built reports summarizing customer behavior using metrics such as conversion rate, average order value, recency of purchase and total amount spent in recent transactions.

These measurements provided general insight into the behavioral tendencies of customers. However, says Deon “In order for retailers to create a meaningful dialogue with customers that honors the shopper’s preferred level and mode of engagement, it takes more than summarized reports, which is why customer intelligence and predictive analytics provide the opportunity to significantly change the retail marketing industry.”

Generic uses of Predictive Analytics are according SAS the following:

  • Detecting fraud. Combining multiple analytics methods can improve pattern detection and prevent criminal behavior. As cyber-security becomes a growing concern, high-performance behavioral analytics examines all actions on a network in real time to spot abnormalities that may indicate fraud.
  • Optimizing marketing campaigns. Predictive analytics are used to determine customer responses or purchases, as well as promote cross-sell opportunities. Predictive models help businesses attract, retain and grow their most profitable customers.
  • Improving operations. Many companies use predictive models to forecast inventory and manage resources. Predictive analytics enables organizations to function more efficiently.
  • Reducing risk. Credit scores are used to assess a buyer’s likelihood of default for purchases and are a well-known example of predictive analytics. A credit score is a number generated by a predictive model that incorporates all data relevant to a person’s creditworthiness.

Erick Siegel of Big Think suggests that predictive analytics allows for a keen assessment of the probability that any one person will buy, sell, click, lie, die, etc. PA doesn’t just predict the future; it can influence it as well.

The challenges of using Predictive Analytics

The big challenge for retailers is to use PA correctly. Not using PA appropriately can cause loss of brand equity and market share with astonishing speed. The key is in understanding the customer’s “digital body language”, suggests Earley (2014) 3. Retailers need to understand customer data – the attributes, needs, characteristics, life stage, behaviour, demographics, and psycho-graphics. The information coming from the data may be used to help customers behave in a way that satisfies their needs 3.

Unfortunately, the use of PA by some retailers has been reported as controversial. Not only are most companies not informing their customers of when and what data they are collecting, but they are not letting them know about their analysis policies, according to Corrigan et al (2014) 4.

According to Arliss Coates from EConsultancy retailers should note the following when using PA:

  • Is automation driving out your innovation and originality?
  • Do you have people that know how to interpret the results of PA?
  • Scenario planning – humans cannot prepare the machines to anticipate every possible nuance or scenario.
  • An over-reliance on data to substantiate decision-making may hampers innovation.
  • The “garbage in, garbage out” principle – bad data will render bad results.

Concluding

The explosion of data is here to stay. At this moment it seems that the availability and use of big data and predictive analytics will grow exponentially. In spite of some controversy and challenges, PA couldn’t have come at a better time for retailers. Predictive analytics may help retailers to integrate their channels more smoothly and thereby keeping in pace with their competitors.

Read also: Big Data for Small Retailers – Is it Doable?

Have a look at this practical demonstration of PA from IBM “”Predictive Analytics for Retail – Introduction”:


Notes

1 Eckerson, W.W. 2007. Predictive Analytics. Extending the Value of Your Data Warehousing Investment, TDWI Best Practices Report, Q1.

2 Waller, M.A. and Fawcett, S.E. 2013. Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management, Journal of Business Logistics, 34(2):77-84.

3 Earley, S. 2014. Big Data and Predictive Analytics: What’s New? IT Professional, 16(1):13-15.

4 Corrigan, H.B., Craciun, G. and Powell, A.M. 2014. How does target know so much about its customers? Utilizing customer analytics to make marketing decisions, Marketing Education Review, 24(2):159-166.

Image

Pixabay

Video

IBM

Chatbots in Retailing – a Fact or a Fad?

Retailers are frequently yelled at by frustrated customers, or, if things go well, they are commended. That’s part of the emotional exchange that comes with a retailer’s job description. However, chatbots may change all of that.

A chatbot is a computer program which conducts a conversation via auditory or textual methods. In other words, sales assistants in a number of retail businesses are now robots. To this end, bots can help retailers in many other ways.

“Chabots are seen as easy and fun ways to help customers achieve an outcome. You’ll encounter them on web sites, social media and even on your smartphone. Say hello to Siri, Allo and Alexa, to name a few”, writes Christine Crandell recently in Forbes.

Siri, Allo and Alexa are computer characters which, through natural language-style dialogs with humans, perform various tasks, such as answering questions, helping them to navigate websites. “They can either look like a human being, or a digital avatar, an animal, alien or may have an image that does not look like a living creature at all” according to ChatBots.org

Apart from retailers not having to face angry customers anymore, the bots allow Bricks and Clicks retailers to catch up on lost sleep. A chatbot is a handy aid for retailers with online customers when their bed time arrives.  “We’d all like to be all things to all customers, but even the most dogged marketer has to sleep sometime”, according to TargetMarketing magazine. The fiction of chatbots has now became a reality as many retailers has bought into the technology.

How chatbots can be used by retailers

Chatbots can be used in many ways by retailers. Nicki Baird (Forbes) suggests that chatbots can do everything – from interacting with customers about new products, to helping them to figure out the trading hours of your shop. Furthermore, leverage chatbots the ubiquity of messaging apps and allows retailers to conduct one-to-one conversations with customers in real-time. Besides, retailers have the opportunity to make money with chatbots.

Ross Simmonds (Crate, Hustle and Grind), identified seven ways retailers can make money with bots:

  1. Bots as a Services (BaaS) – help people and teams to be more productive. They can manage tasks or tackle communications challenges – by replicating business models already in use;
  2. Bots plus sponsored and native content – native or sponsored content is a model in which brands pay to have their content distributed by media companies directly into their channels;
  3. Bot leveraged affiliate marketing – for example: retailers can develop a bot that offers tips and tricks on how to stay healthy and use affiliate links to send people to fitness products that have affiliate links associated with them;
  4. Bots for research – there are bots that you can pay to do the research for you.
  5. Bots for lead generation – may act as a lead generator with an initial focus on content. Chatbots designed to deliver insights and information to users who are looking for advice or information can be lined up with products that the retailer offers;
  6. Pure retail sales bots – the user will make the purchase directly through a chat with the bot and it will act similar to a transaction from a typical website;
  7. Cost per conversation/task – as bots become more sophisticated, people may be willing to pay to have conversations with the bots that can help them with various challenges in life.

“Thanks to big data, artificial intelligence (AI) and predictive analytics, as well as the proliferation of messaging apps, retailers finally have the tools (including chatbots) to get the right messages to their customers”, suggests Craig Alberino in TotalRetail. However, the chatbot hype is not favored by everyone…

Consumers that use  chatbots can complete a purchase in a minute or two. Have a look at the video from Kore:

The future use of chatbots

Although the use of chatbots is getting much attention nowadays, not everyone is excited about it. Jon Evens writing last year in The Walrus reminded us of the “Eliza effect: “Humans unconsciously assume that software which communicates conversationally has much more intelligence and sophistication than is actually present.

Inevitably, the software eventually fails to match that assumption, disappointing and frustrating the user who unconsciously expected more.” Consequently, your frustrated customers may want to communicate (again) in person with you. Because the computer does not understands… Above all, what is good and bad about chatbots? Quora.com responded as follows:

The Good Things about Chatbots The Bad Things about Chatbots
1.       Chatbots are a good alternative for mobile apps 1.       Chatbots have a high error rate
2.       With bots, nothing new needs to be learnt 2.       Chatbots don’t put people first
3.       Bots are capable of providing a great user experience 3.       Bots are limited in their capabilities
4.       Chatbots as the factotum for all business needs 4.       Chatbots aren’t as intelligent as humans

Concluding

In summary, are chatbots the “silver bullets” that retailers can use to compete in a digitized retail environment? Or will it be another fad with demanding customers not getting assisted properly? I suppose we have to wait and see. However, Leo Sun (fool.com) recently asked: “Were the social network’s chatbot ambitions ahead of their time?”

Importantly, this is after Facebook is reportedly scaling back its chatbot efforts on Messenger after the programs failed to fulfill 70% of users’ requests. Consequently those requests couldn’t be handled without human agents, and bots built by outside developers “had issues” because the “technology to understand human requests wasn’t developed enough.”

Finally, perhaps Dale 1, (2016) sobering comment can be noted by all: “If we want to have better conversations with machines, we stand to benefit from having better conversations among ourselves.”

Additional reading:

  1. Artificial Intelligence – Digital Outcomes or Digital Disruptions for Retailers?

Note:

1 Dale, R. 2016. The return of the chatbots. Natural Language Engineering, 22(5):811-817.

Image and video:

  1. Pixabay
  2. Kore