Tag Archives: predictive analytics

How the Fourth Industrial Revolution Shapes the Retail Industry

It seems to almost happen overnight, the Fourth Industrial Revolution. “Something’s coming. It’s picking up speed and will overtake most of us in the coming years. Industries will be disrupted, lifestyles will be altered, economics will be shattered as this revolution takes hold”, according to a press release by the Digital Journal.

The revolution started almost unnoticed, very quiet and extremely swiftly. It’s also clean, unlike the first industrial revolution that was noisy, dirty and very slowly – just imagine the coal gobbling steam engines, 3 centuries ago…

Don’t make any mistake. The 4th Industrial Revolution wouldn’t have happened without the 1st (coal); the 2nd (oil and electricity) and the 3rd (internet technology and clean energy) Industrial Revolutions.

However, the Fourth Industrial Revolution is busy happening right now and it’s having a profound effect on our businesses and our lives. Indeed, the Fourth Industrial Revolution and the future of core technology trend are expected to result in an all-new era of automated industries 1.

Also, the retail industry and its customers are already part and parcel of this revolution.

So what is the Fourth Industrial Revolution?

The internet and Information Communication Technology (ICT) have facilitated the advent of cyber-physical Internet-based systems.  These systems offer innovative capacities that can benefit industry and other economic sectors. This phenomenon is happening now and is known as the 4th Industrial Revolution.

The Fourth Industrial Revolution is a fusion of all current technologies to create a cyber-physical system 1.

Floridi 3 (2014) explains the 4th Industrial Revolution as a space where smart and autonomous agents no longer need to be human.  Therefore, a society that’s fully dependent on third-order technologies and thus are human-independent. Here, learned machines that communicate with each other, are taking over the thinking and doing of humans…

Or, as Oosthuizen 2 (2016) recently described it: “Consider the possibilities of mobile devices connecting billions of people driving unparalleled processing power, storage capabilities and access to knowledge. In addition, the overwhelming convergence of emergent technology such as, among others, artificial intelligence (AI), robotics, the internet of things (IoT), autonomous vehicles, 3D printing, nanotechnology, biotechnology, materials science, energy storage and quantum computing.”

The retail industry is one of the spaces in business that the 4th Industrial Revolution is seen working and it is experienced by many.

How the Fourth Industrial Revolution shapes the retail industry

The World Economic Forum 4 (2015) identified six software and services mega-trends which are shaping society:

People and the internet

How people connect with others, information and the world around them is being transformed through a combination of technologies. Wearable and implantable technologies will enhance people’s “digital presence”, allowing them to interact with objects and one another in new ways.

Bricks2Clicks recently discussed how the multi-purposed smartphones of customers that are AI empowered can help them to connect, communicate, recognize and experience the digital world of the 4th Industrial revolution.

Computing, communications and storage everywhere

The continued rapid decline in the size and cost of computing and connectivity technologies is driving an exponential growth in the potential to access and leverage the internet. This will lead to ubiquitous computing power being available, where everyone has access to a supercomputer in their pocket, with nearly unlimited storage capacity.

The use of the mobile smartphone has grown exponentially since its introduction a decade ago. In fact, just over 36 percent of the world’s population is projected to use a smartphone by 2018, up from about 10 percent in 2011, according to Statista. For retailers the growth in use of smartphones by their customers may result in opportunities and threats. Read more: Bricks and Mortar Retailers Need To Be Smart With Smartphone Customers.

The Internet of Things (IOT)

Smaller, cheaper and smarter sensors are being introduced – in homes, clothes and accessories, cities, transport and energy networks, as well as manufacturing processes.

“Connected devices and products provide retailers with the opportunity to help optimize operations in the face of a more complex supply chain. That’s  increasingly important for digital channels, and a more demanding customers.  By utilizing the IOT, managers can track inventory more easily, and adjusting pricing in real time using smart tags”, says Douw G Steyn (Bricks2Clicks).

Artificial intelligence (AI) and big data

Exponential digitization creates exponentially more data – about everything and everyone. In parallel, the sophistication of the problems software can address, and the ability for software to learn and evolve itself, is advancing rapidly. This is built on the rise of big data for decision-making, and the influence that AI and robotics are starting to have on decision-making and jobs.

Retailers will have to decide where and when Artificial Intelligence has the potential to replace human intelligence. Cost and scale will drive these decisions. Future decisions about AI by retailers will probably be about the ethics of using the technology and the effect it may have on society. Further reading: Artificial Intelligence – Digital Outcomes or Digital Disruptions for Retailers?

The sharing economy and distributed trust

The internet is driving a shift towards networks and platform-based social and economic models. As a result, assets can be shared, creating not just new efficiencies but also whole new business models and opportunities for social self-organization. The Blockchain, an emerging technology, replaces the need for third-party institutions to provide trust for financial, contract and voting activities.

Here we are talking about crypto-currencies such as Bitcoin. “There is the potential for a lot of demand for crypto-currencies from a consumer perspective. But right now it’s a pretty complex process to set up a digital wallet, gain access to a crypto-currency exchange, and start buying up coins”, according to Nikki Baird (Forbes).

The digitization of matter

Physical objects are “printed” from raw materials via additive, or 3D, printing, a process that transforms industrial manufacturing. Consequently it allows for printing products at home and creates a whole set of human health opportunities.

3D printing technology for retailers is now emerging as an outcome for small localized retailers that are facing closure. However, as it is with most disruptive technologies, the advantages that 3D printing offer for retailers should be weighed against its potential pitfalls. Read more: 3D Printing Technology for Retailers – An Opportunity or a Waste of Money?

Conclusion

The 4th Industrial Revolution is not only about digital technology and gadgets, but also about us. How should we prepare ourselves and our children to survive and prosper in this digital, robotic and information rich space? And what about retail? Not only need the structure, operations and organisational cultures change at retailers, but retailers also need extraordinary leaders (Read: Success in the Digital Age Requires Extraordinary Retail Leaders).

The last words are from Albert Einstein: “The distinction between the past, present and future is only a stubbornly persistent illusion.”

Notes

1 Chung, M. and Kim, J. 2016. The Internet Information and Technology Research Directions based on the Fourth Industrial Revolution, KSII Transactions on Internet & Information Systems, 10(3):1311-1320.

2 Oosthuizen, J.H. 2016. Entrepreneurial intelligence: expanding Schwab’s four-type intelligence proposition to meaningfully address the challenges of the fourth industrial revolution. In proceedings of 28th Annual Conference of the Southern African Institute of Management Scientists, University of Pretoria, South Africa.

3 Luciano Floridi 2014. The Fourth Revolution, How the Infosphere is Reshaping Human Reality, Oxford University Press, USA.

4 World Economic Forum 2015. Deep Shift: Technology Tipping Points and Societal Impact, Global Agenda Council on the Future of Software & Society, Survey Report, World Economic Forum, Geneva, Switzerland.

Images

c1.staticflickr.com and pixabay.com

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