Tanya Long, COO Argility -retail concepts and resources

Predictive analytics lights the way

Retailers and brands can realise the immense power of data-driven decision-making by using predictive analytics to glean faster insights.

By Tanya Long, CEO – Argility Technology Group

Previous articles published on ITWeb have explored some of the challenges and solutions for retailers’ consideration to limit the impact of load-shedding on operations. The Industry Insights examined how technology, augmented by partnering with trusted suppliers, can be used to help navigate this, but there is more that can be done.

There has been a radical change in production, supply chain and consumer behaviour which cannot be ignored. And here is where we lean on the technology innovations of this century: artificial intelligence (AI), machine learning, predictive analytics and cloud
solutions.

It is true that predictive analytics is not a crystal ball; however, the immense power it can give retailers and brands cannot be ignored.

To use a casino analogy as an example: Let’s say you play roulette. In years gone by, one would watch the tables, evaluate the trends of each croupier, and then decide at which table you would take your chance with your R100. Imagine if someone had whispered to you – ‘Table 1: 65% probability on red, 35% probability on black’. What decision would you have made? And if an insight like this was given every 15 minutes, would you have been inclined to include this in your next decision for action?

Predictive analytics is not a crystal ball; however, the immense power it can give retailers and brands cannot be ignored.

This is the advantage that predictive analytics can provide. In a nutshell, it is the power of data-driven decision-making.

With machine learning, fluctuating behaviour can continuously be used to understand supply and demand patterns with a speed that is incomprehensible when compared to five or 10 years ago. The traditional software approach was to create the system rules based on the known rules; however, with the increased landscape complexity, an intelligent approach is to use machine learning to identify the patterns and provide much deeper insight.

It’s often said the beauty of AI is that it never sleeps – it works 24/7 – just what is needed to help us in our current economy.

Retailers and brands now have the ability to glean faster insights from multiple inputs relating to stock availability, pricing, placement and customer demand.

Predictive analytics uses the principle of small, compounded gains. For example:

  • What positive impact would be experienced by having a 2% increase in stock-on-shelf availability?
  • What impact would be gained by having a 2% increase in cash flow due to lowering of aged stock surpluses?
  • What would a 1% increase in gross profit mean, if stock prices were able to be speedily and strategically managed by considering pricing appetite and competitor pricing across a basket of products?

Now, I realise all of this takes data, and prevalent discussions in the media has raised the question: how much data do I need? When do I start using analytics? The general consensus is if one hasn’t started yet, you are already backfooted.

The advent of cloud has given the ability to store and process petabytes of data, cost-effectively and without excessive hardware investment. It has also given us the ability to scale easily with increasing/changing needs and to leverage off always-on infrastructure provided by global players.

What problems can predictive analytics solve?

Let’s start with the concept of demand forecasting. In the old days, stock and sales forecasting were generally formulated based on moving averages. Today’s retailers would be ill-advised to continue in the same vein. Predictive analytics can add immense value in this area.

Another area that predictive analytics can play a huge role for retailers is focusing on customer patterns. Not only for personalisation by getting the right product in front of the right customer, at the right time and right price, but also to evaluate the change in shopping behaviours.

Using machine learning for clustering and segmentation, identifying purchase habits and preferred products would streamline a personalised recommendations strategy.

In a credit retail environment, the additional insights on areas such as propensity to buy and customer lifetime value has the potential of elevating prospecting efforts. Anticipating customer needs would definitely not only assist retailers on building customer loyalty, but also decrease the cost of spray-and-pray marketing efforts.

Labour management is another area where data and analytics can play an important role in improving operational efficiencies. One cost pressure which may be within a retailer’s control is looking at optimal workforce levels.

Gathering data on speed and accuracy of staff performance can allow retailers and brands to streamline their operations to efficiently meet fluctuating business demands.

These are by no means where the use-cases end. I suggest commencing in an area where there is a compelling business challenge and in which one can measure and refine the results in order to take appropriate action.

Business and project sponsor support is vital for success, although I believe many stakeholders would rather be proactive and stack the odds in their favour.

Source: IT Web

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Demystifying category management for small retailers operating in low data environments.

Challenges in implementing effective category management strategies.

By Bernhardt van der Merwe, head of Category Management

Retail is a vibrant and highly competitive sector in which small businesses often face challenges implementing effective category management strategies. This is often attributed to SMEs limited access to detailed data.

By developing your understanding of the core principles of category management, you can adapt them to your unique circumstances. Moreover, arming yourself with knowledge can help you make better, more informed decisions that drive sales and enhance customer satisfaction.

Unpacking category management

It is a strategic approach to retail business management that focuses on product categories as individual business units. It involves managing product categories to maximise sales and profit, which in turn can require detailed data analysis.

However, small retailers might not have access to granular data. This is where it is important to note that limited data does not translate to ‘no data’ – there are ways in which you can leverage what you have to assist you in making informed decisions.

Let’s commence with what is possibly the most important data – sales, and how to make the most of it. Never forget that even the most basic sales data can be a veritable information goldmine. Start with the data you have, even if it’s very basic – by combining it with the right strategies, you can still effectively manage your categories.

The following are my recommendations for strategies you can deploy to unlock the value of category management in your business:

  • Start by listening to your customers – direct customer feedback can be incredibly insightful. You need to encourage customers – possibly even incentivise them in some small way through a competition, etc., or offer a potential discount off their next shopping basket. Basically, devise ways to encourage them to share their thoughts through feedback forms, surveys, and even casual conversations. The trick is to pay close attention to their preferences, complaints, and suggestions. Throughout my career, I have conducted numerous such exercises in person. The information gathered, albeit informally, has often been sufficient to enhance sales performance when applied effectively. While not diminishing the value of formal research, I have come to believe that informal customer feedback is an essential starting point for investigating ways to improve the point-of-sale experience.
  • Next, listen to your staff – they are a valuable source of information and are also your goodwill ambassadors. You must motivate them by encouraging them to reveal their observations and insights. Merchandisers, for example, spend most of their time in front of shelves unpacking and rearranging products. In this way, they have a direct view of how shoppers buy, what they are looking for, and what frustrates them.
  • Build supplier relationships – developing strong partnerships with suppliers is key to opening the door to valuable information, as suppliers have access to the broader market and shopper insights. They can provide invaluable data on product trends, shopper preferences, and industry benchmarks.
    Customer insights can inform how products should be grouped and sequenced to enhance on-shelf visibility, thereby addressing complete solution offerings or fulfilling specific needs. By strategically grouping products on the shelf, retailers can potentially encourage customers to purchase more items or opt for higher-value products, effectively increasing the total value of each sale.
  • Inventory turnover analysis – investigating how quickly items are sold and restocked can offer insights into the performance of specific items. Top product sellers are usually your best performers and typically require more frequent replenishment – such items can benefit from increased shelf space.
    Such analysis can guide your stocking decisions while also providing perspective on how poorly performing products, with extensive shelf space, are appropriating both space and visibility to the detriment of better performing items. This can result in revenue tied up on the shelf, high replenishment rates, frequent out-of-stock situations, and an often-overstocked back room.
  • Technology tools for small retailers – are available. Your budget may not stretch to sophisticated category management software, but basic analytics tools are readily available and user-friendly. Such tools can help you track sales, monitor inventory levels, and analyse customer buying patterns.
    A good example is Excel – a spreadsheet programme from Microsoft and a component of its Office product group for business applications. Microsoft Excel enables users to format, organise and calculate data in a spreadsheet. By organising data using software like Excel, data analysts and other users can make information easier to view as data is added or changed. It is widely accessible, user-friendly, and can be used for basic analytics. Many businesses, especially smaller ones, use Excel as a cost-effective tool for these purposes, making it a practical choice when more sophisticated category management software is not within their budget or capabilities.

Test and learn

The question now is: how do you get the ball rolling? I advise you to begin by focusing on the changes most likely to yield the most promising results. Start with small changes in your product range and merchandising strategies – then scrutinise the results. You’ll need to work out whether the methodology you used to effect the changes can also serve as best practice.

If the answer is ‘yes’, then you can go ahead and apply to similar categories. Because it’s crucial that you remain informed about technology and general trends in your sector, you will need to factor in an investment in more advanced data collection and analysis. But this is less daunting than you think, because you can fund it from the inevitable growth your business will enjoy through continuous refinement of your approach to category management.

So, to reiterate, the implementation of category management in a low data environment is not without its challenges, but it’s important to understand that it can be done, and in doing so, you can unlock a treasure trove of business benefits. You know the old phrase, ‘from little acorns mighty oaks grow’, my advice is to be strategic in your thinking and start small, incrementally building over time.

The rewards of this approach are proven to be immense, including enhanced business performance through better decision-making.

Source: BizCommunity

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Boards-of-Directors - Data & Analytics

Boards of Directors have spoken! What challenges will data and analytics leaders face?

By Andrej Hudoklin, Executive Head of Data and AI: Europe

From accelerating digital business to much-needed cultural changes, data and analytics will face serious challenges in 2021. Given that every digital business moment is powered by, or held hostage to, data and analytics, it is no wonder that chronic issues have become more acute.

3 Trends that will impact data and analytics leaders in 2021:

The Heat Is On – Boards of Directors Place analytics and AI as the No.1 and No. 2 Priorities

*The Heat Is On – Boards of Directors Place analytics and AI as the No.1 and No. 2 Priorities; source: https://www.gartner.com/en/publications/data-analytics-top-priorities-for-it-leadership-vision-2021

  • Chief Data Officer will embed data and analytics in business strategy. They are responsible for guiding decision-makers within an organisation using data insights.
  • Data literacy increases. It is about developing a common understanding of what goals and outcomes are important across the organisation.
  • Data and Analytics will change management. You must base your data and analytics organisation on collaboration, cooperation, and problem-solving. Let us spread the word about how data and analytics can help drive business outcomes.

Data and analytics leaders will face many challenges in 2021, such as embedding data and analytics into business results and addressing perceptions of data and analytics within the organisation.

The best actions for data and analytics leaders are to create a data-driven culture, develop a data and analytics strategy, and stand up a data governance program. They must implement adaptive data and analytics governance. Adaptive data and analytics differ from traditional approaches that tended to be IT-driven and focused on data and standards. Adaptive governance uses business outcomes to prioritise the work and helps you govern the least amount of data with the greatest business impact.

Leaders must answer 4 crucial questions:

  1. Build a data and analytics strategy and operating model that can support and scale, with a measurable business impact?
  2. Apply trusted data and analytics in their decision-making?
  3. Create a business case to support data and analytics monetisation and innovation?
  4. Develop a data and analytics-driven culture, literacy, and employee behaviour?

So, the heat is on. According to Gartner, the top game-changer technologies to emerge stronger from the COVID-19 crisis are Analytics, Artificial Intelligence and Autonomous Things.

Leading in 2021 will look different from leadership in 2020, but understanding and planning for unknowns and continued disruption across the IT organisation is critical for moving forward.

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