AI and the omnichannel approach for retailers

ai

Blog Written By | Progressive Grocer 

Grocers that miss the omnichannel wave of disruption risk wipeout

The grocery industry is notorious for tight margins, high perishability, and heavy reliance on promotions. It’s a prime target for competition, which is why grocers are facing wave after wave of disruption.

The first came years ago when Amazon and other eCommerce businesses began eating away at the edges of grocers’ already slim profit margins by competing on purchases of non-perishable consumer goods.

The second bigger wave is hitting now, in 2019, as the “delivery economy” has added additional dimensions for grocers to consider. Grocery delivery can be a cash cow, but it also poses serious logistical challenges, from inventory management to storage. Direct-to-consumer meal-kit delivery services like HelloFresh pose a competitive threat, too. Promising fresh, quality ingredients landed directly on consumers’ doors without the need for grocery shopping, these apps appear to cut out the middle man.

But the second wave of disruption that grocers are facing goes beyond that. Take a step back and consider the bigger picture. July marked the two-year anniversary of Amazon’s purchase of Whole Foods Market. Now, the eCommerce giant appears to be exploring a new chain built for both in-store shopping and pickup and delivery.

Meanwhile, apps like Instacart have democratized delivery across the economy, creating on-demand consumer convenience at the push of a button. A premium has been placed on the retail experience, online and off, now that customers rely on multiple methods to get what they need, depending on what’s easiest for them at the time.

Omnichannel shopping has finally gone mainstream. There’s no looking back. The future of grocery and retail is now, and consumer expectations have never been higher. 

So the question then becomes. how can legacy grocers keep up with the speed of change? These industry-wide changes are being driven externally, after all. What can grocers do to keep up with the innovation that’s being driven by the modern tech giants?

By providing shoppers with the convenience and personalization they crave, using the vast stores of information at their disposal, grocers are in a unique position. They have the opportunity to fight back using technology against technology.

Using artificial intelligence (AI), grocers have the capability to grow their profit margins by improving demand forecasting, making promotions more personalized and thus more effective, and streamlining their business to keep up with each new wave of technology-enabled disruption.

The fact is, grocers know a lot about consumers – what type of beer they like, their favorite pasta sauce, and f they prefer almond or whole milk. It’s all listed and recorded across mountain ranges of data year over year, category over category.

How AI technology can guide customer insights and recommendations

Studying this data, grocers can find trends. They may find regional or seasonal patterns, for example. Or maybe they can find complementary goods, increasing the chance to raise the all-important basket price with the right promotion.

Using a blend of AI and machine-learning models, grocers can supercharge this type of analysis. What might take weeks or months of analysis done manually can be cut substantially as the right AI technology enables both automated and higher-level decision-making.

Now, not only can grocers find trends at the product, category or store level, but they can also find personal shopping trends. And with this level of insight, grocers can make more financially lucrative business decisions that meet the demands of their most discerning customers.

For example, a grocer might use AI models to determine which products to include in its weekly circular ads, what order they should place them in, and what level of promotions they should offer for each item. Or they may decide to develop models that suggest personalized product recommendations and promotions based on triggers determined from past behavior – and integrate these into mobile apps that push notifications out at certain frequencies.

In this way, AI can also improve omnichannel strategies like click-and-collect by suggesting that customers pick up a discounted dinner when they pick up their groceries, incentivizing the customer to use the retailer in other ways.

The use of AI in grocery is really just the beginning. The future of grocery may have just arrived, but what grocers make of it is really up to them. Yet one thing is clear: Grocers that succeed in this new world of disruption will surely be the ones that start innovating now.

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What does the future of AI look like in the Enterprise?

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Blog by | George Brown, Jet Global

The Future of AI in the Enterprise

The business world is at an inflection point when it comes to the application of Artificial Intelligence (AI). While the technology behind enabling computers to simulate human thought has been developing, at times slowly, over the past half-century, the cost of implementation, readily available access to cloud computing, an practical business use cases are primed to help AI make a dramatic impact on the enterprise over the next few years.

With the potential use cases on the horizon for AI in business, as well as the investment dollars and rate of change currently propelling AI, one thing is clear: you’ll need to get your foundation in place sooner, rather than later, to take advantage of the benefits coming to the business world.

Enter business intelligence (BI) software. By building the foundation now with this readily available, accessible, and affordable software, businesses can prepare themselves for the future while also reaping the benefits of today. After a couple of years with inflated expectations for AI that have yet to materialize, businesses are beginning to ask themselves whether it makes sense to push through a costly implementation that won’t yield tangible results for 2-3 years – when really, they should be focused on implementing BI today, yield results immediately, and layer AI on top of your established  BI data to derive new insights and drive greater benefit once the technology matures/.

So how will BI software help set the stage for AI in your enterprise, and what possible use cases can be gleaned from the intersection of AI and BI?

How Can BI Software Help?

Regardless of where you’re landing in regards to Artifical Intelligence and Business Intelligence, one thing is true: you’ll need to have data to feed to both. Without data to act upon, there’s no ‘intelligence’ in AI or BI. There’s nothing to analyze, or apply a learning algorithm to – when it comes to any intelligence solution, data is the foundation upon which it must be built.

Thankfully, with the widespread of cloud computing and the Internet of Things (IoT), data has never been more readily available in today’s business world. But the vast reams of data generated on a daily basis are presenting a new problem for businesses – what does it mean and which data matters? How should data be tagged, sorted, grouped and analyzed? Which problems do disparate data points speak to? And how can the data be collected across multiple touchpoints, from all retail locations to the supply chain to the factory be easily integrated?

Enter data warehousing. Data warehouses are a means of taking data points from disparate touchpoints (such as point of sale, CRM, inventory, and warehouse management systems), standardizing the data collected, structuring it to extract necessary insights, and running analysis. Enterprise businesses cannot survive without robust data warehousing – data silos can rapidly devour money and resources, and any business still trying to make sense and cobble together ‘business intelligence’ from multiple reports and inconsistent data is rapidly going to lose ground to those businesses with integrated data and reporting.

The optimized data warehouse isn’t simply a number of relational databases cobbled together, however – it’s built on modern data storage structures as the Online Analytical Processing (OLAP) cubes. Cubes are multi-dimensional datasets that are optimized for analytical processing applications such as AI or BI solutions. Cubes are superior to tables in that they can link and sort data by multiple dimensions, allowing for non-technical users to choose from any number of role-specific and highly contextual data points to uncover new insights and adjust tactics and decisions on the fly. Chances are good that your average non-technical sales agent or purchasing representative will have difficulty joining multiple tables together with a standard report, but with Business Intelligence cubes, all that is required is to drag and drop the metrics and dimensions that matter to them into their own personalized dashboards.

So how is the data extracted? By using Structured Query Language (SQL), the language used to manipulate and extract data stored in cubes. SQL was developed as a standard language to communicate databases, regardless of exactly which type of database was being used, and is ultimately the means by which data in a table is extracted, deleted, updated and managed.

Beyond data warehousing and OLAP cubes, which provide the technical foundation, there are a number of additional components that can help enterprise businesses address their data requirements”

Data modeling: Data modeling is a method of mapping out individual data sources across an enterprise and determining how they need to interact with one another to extract the most valuable business insights. Data modeling can be performed at the conceptual (high-level, related to business objectives), logical (mapping to each business function), and physical (how the actual dimensions, measures, and hierarchies are related within a data cube).

Analytics and reporting: Capturing, structuring, and storing data is good – but being able to analyze and report on it is the ultimate end goal. Business intelligence solutions are capable of providing simple, accessible analytics and reporting functions for end-users, empowering them to find actionable insights they need with little technical expertise (or formal data science training). This also helps business functions avoid unnecessary data logjams and giving them instant access they need to the data they so desperately require.

Data visualization and dashboards: Analytics and reports are a crucial component of business intelligence, but if you’ve ever spent hours poring over a table of values trying to decipher exactly what the data is saying, you’re not alone. With data visualization tools, critical insights are displayed in rich graphical representations that are vastly easier for the human brain to interpret. According to a study by Aberdeen Group, organizations using visualization tools are 28 percent more likely to find timely information than those who rely solely on managed reporting; the same study also found that 48 percent of business intelligence users at companies with visual data discovery are able to find the information they need without the help of IT staff. Dashboards can easily assemble visualizations and reports into customizable displays by end-user or business units, giving individuals instant insight into KPIs that help drive better business performance from the bottom up.

Security, simplicity, speed – these are the three major benefits business intelligence solutions help to drive, and three critical measures of success in enterprise business. While artificial intelligence remains focused on helping computers glean insight entirely on their own, business intelligence is enabling entire organizations to gain access to the data they need to make rapid, informed decisions, and the importance of that in today’s quickly shifting business landscape simply can’t be overstated. In a survey of 2,600 business intelligence end-users, 91% responded that BI gave them faster reporting, analysis or planning, 84% said it enabled them to make better business decisions, and 79% said it improved employee satisfaction.

The Future is (Almost) Here

In the near future, AI algorithms will be able to be seamlessly applied to your existing data stores, unlocking further insight for your enterprise. As highlighted in this 2018 Harvard Business Review article, AI applications in response to business needs fall into one of three categories:

  • Process automation: The most common current application for AI in business is by automating systems and business processes. While previous incarnations of automation focused on exchanging information between systems, AI can level up this ability by actually interacting with the data like a human – either inputting or consuming as necessary. Today, AI ‘robots’ are able to analyze legal contracts and extract relevant provisions, update customer records across a number of disparate systems, and automate customer outreach in response to situational conditions. As these algorithms grow ‘smarter’, businesses will be able to automate even broader swaths of processes.
  • Cognitive insight: Cognitive insight is the ability to apply AI algorithms to vast existing stores of data to extract meaning and identify patterns. While BI software and data stores will undoubtedly provide the ‘diet’ for cognitive insight algorithms, as the algorithms learn, they’ll be able to apply those learnings to broader data sets, react to new data in real-time, identify potential data matches across multiple databases, or manage programmatic ad buying.
  • Cognitive engagement: Cognitive engagement refers to the human-interfacing element of AI – think automated chatbots, knowledge bases, product recommendation engines, and more. Cognitive engagement applications can be used to automate interactions between people and systems, either externally (for customers), or internally (for employers). Most current applications focus on internal engagement as businesses are still apprehensive about the relatively new applications – but, again, as AI development and implementations continue to mature, expect objections to fall by the wayside as businesses will find new ways of using existing data to drive meaningful automated interactions with human beings around the world.

Over the next few years, you’ll see artificial intelligence finally begin to live up to the hype we’ve been hearing about in the business world, and computers will help usher in a new era of productivity and profitability for enterprises on the cutting edge – but only if you have the foundation in place today, and that starts with business intelligence.

Don’t hesitate – ensure you’re setting up your business tomorrow for success today with Jet Global and allonline365. Contact us on  info@allonline365.com or  +27 (21) 205 3650.

www.allonline365.com

Reducing Supermarket Food Waste with Dynamic Pricing

dynamic pricing

The ability to react and cater to consumer demand is nearly always a competitive advantage, but it also comes at a price. Naturally, when a shopper heads to the store to buy their groceries, they expect everything on their list to be available, and their products fresh.

But as supermarkets manage a growing number of products with varying expiration dates, they’re faced with the increasing challenges of selling these items before they expire while making sure they generate as much profit as possible. Failing to accomplish this not only hurts the retailer’s bottom line but also leads to unnecessary food waste. In fact, one-third of the food produced for consumption is lost or wasted globally, and grocers are significant contributors.

In order to save sales and limit loses, grocers often incentivize shoppers to make purchases by discounting products nearing their sell-by date. However, the challenge is that they often struggle to find the perfect pricing and timing balance that will maximize sales and also minimize the discount they need to offer. They walk a thin line of discounting too early or offering discounts that are too high.

With hundreds of thousands of SKUs, internal teams simply can’t manage the complexity of this process. Fortunately, the emergence of AI-enabled solutions makes a responsive price strategy on perishable food far more simpler and more profitable.

Let’s explore how artificial intelligence allows grocery retailers to bypass old roadblocks by enabling dynamic pricing strategies that pad margins and reduce food waste.

Traditional limitations to pricing

Historically, responsive price adjustments were a hassle, or even impossible, especially in physical stores. The process relied on rules-based algorithms that required significant manual oversight.

Picture the pricing gun – employees have to manually walk through the store and change the price on each individual product if there is a sale or special offer on these items. For supermarket and food retailers, the selling period for marked down products is very short, creating a major pain point when attempting to optimize prices in real-time.

These obstacles have led to the development of new tools that automate efforts around identifying optimal prices and enable retailers to make the resulting adjustments quickly.

Optimizing price around sell-by dates

Today, the use of artificial intelligence has led to breakthroughs in markdown pricing. Aggregating demand behavior (historical and current) with inventory information, competitor pricing and sell-by dates allows for pricing strategies that can be optimized in real-time, across all areas of business, and at scale.

For example, bananas that begin to ripen need to be sold while consumers are still willing to pay for them, but not before a new shipment comes in. Providing discounts to shoppers on products that are still fresh gives shoppers an incentive to buy now. This can even be done on a granular, individual store level, as inventory and consumer buying patterns vary based on store geographies. What this means for customers is that products are available at any time at the best possible price, ultimately leading to less wasted food.

Increasing profitability through product affinity

Retailers can also increase profitability by optimizing pricing on products with strong affinity. Analyzing data on products that are sold together, retailers can detect cross-sell opportunities and markdown one product while driving sales of other related products at full price.

As shoppers head to the grocery store for the fourth of July, retailers can find areas to increase sales with perishable and non-perishable items typically paired together. For example, offering a markdown on cherries nearing their sell-by date, if bought with a pie crust, or similarly, full-priced hotdogs and discounted buns.

By linking multiple items, retailers can lower prices of certain products that may have otherwise expired while still increasing the bottom line by ensuring sales of higher-margin items.

Empowering retailers to reduce food waste and better serve shoppers.

When a business deploys dynamic pricing, they become more sustainable – both environmentally and financially. But it also has the benefit of generating loyalty among shoppers who receive the best deals and competitive prices. In addition to time-sensitive food, retailers can also see the advantages of markdown pricing for seasonal items or products with short selling cycles.

With dynamic pricing driven by AI, retailers can gain a holistic view of their entire inventory in real-time, as well as the connections between products, and can optimize their strategies on the fly in order to reduce food waste and better serve shoppers.

allonline365 specializes in retail solutions to help manage your business. We offer solutions that address your current business needs as well as your future ones. Choose a solution that digitizes your business and grows along with you. Contact us on  info@allonline365.com or  +27 (21) 205 3650.

www.allonline365.com

Resource Credit | Progressive Grocer 

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