Posts Tagged ‘BI’

Fashion Forward—Part 5: Linking Business Intelligence with Strategic Execution

by Richard Mills • July 14th, 2011 • Write a comment »

In retail today, the sheer amount of consumer shopping data coming into the business is overwhelming with data from every channel, lying in multiple facets of operating systems, crossing each department of your business. Even stores that traditionally carried basics or relied on single allocations per season are dabbling in some form or another with fast fashion because newness is what brings customers into the store, and that newness is what gives retailers a unique edge over the competition. But when you’re exploring quick-turn merchandise, your real-time data is even more important.

It isn’t surprising that these retailers are seeking business intelligence (BI) tools to simplify that data into usable knowledge for more insightful decision-making. Most of these traditional BI tools gather and provide insight and information into achieving business goals, but it’s linking the business intelligence with execution that actually improves performance.

Business complexities

Though BI tools are often the go-to solution for many retailers, even the best tools are not enough to help retailers make optimal decisions. The complexities in retail today require these tools to do more than just give an answer; they need to trigger an action.

Having more than one customer with different needs and wants is just the beginning. There are increasingly more and more channels of customer behavior to understand along with multiple store formats for brick and mortar stores as well as new purchasing outlets: e-commerce, f-commerce, mobile commerce, catalogs, and social media. There is no longer one single way to buy and communicate.

The business complexities don’t stop there – the inventory placement decisions retailers have to make on a daily basis are more daunting than ever. For example, let’s say you have 500 stores with three buys or receipts, multiplied by 10,000 SKUs; you’re looking at 5,000,000 decisions to make each day.

Data proliferation

This complexity breeds data proliferation. The new consumer channels and store formats provide ample amounts of information. There is so much data coming in that retailers are struggling to put it into context for solving a problem and are struggling to keep up with how shoppers are behaving on a daily basis. Therefore, it is essential to understand that historical sales don’t mean as much when behaviors and customer patterns are as erratic as they are today. Retailers need to be able to monitor how their customers are acting now. This means that BI tools need to be able to create and react to shopper insight as quickly as possible.

And when you are dealing with fast fashion you don’t have the liberty of comparing the historical sales data, because the styles and colors of last season are not applicable to the wants of your customers today. You would be shooting yourself in the foot if that were the only data you based your decisions on. You need to utilize real-time demand by transforming it into an action plan for allocating and replenishing your stores.

Failure of traditional tools

BI needs to be actionable and align these actionable decisions with tactical execution, which old systems don’t do. They also don’t give visibility to lost sales; they don’t see the areas where they could have more sales but missed out due to inventory and real demand. Most systems don’t understand product lifecycles, especially in fashion when the demand you’re seeing at the moment isn’t necessarily a true reflection of what you’ll see next week. Lastly, traditional tools remain static instead of dynamic ceasing to learn over time. Instead, they only give a limited picture of the frame of time the user inquires about.

With all that said, in order to make the most of BI, retailers need to have a strategy in place as to how to execute actionable merchandising decisions in the most effective way.

Linking the science of BI with the art of merchandising

In order to link the science of BI to the art of merchandising, retailers need to start asking strategic questions and understanding a number of components. How can that be done? It is critical to define your strategy: what are you trying to do with a product, why are you buying it, and why is it important to your assortment? In other words, determine your product’s role, goal, and operational constraints. Keep in mind that any strategy must be put into a business context and executed in a defined business process that ensures you deliver business value. Continuously analyze behaviors and use it to make predictions.

You also need to imbed the strategy into the technology, not just the process, making everything part of your systems and eliminating the gap. This, in turn, creates visibility of your business context so you know why things are happening especially when they aren’t going right. BI should also provide all the information necessary for decision-making by putting it together and eliminating data choosing.

Better data leads to better decisions

There are principles that should be applied that address and deal with all the issues of today’s retail and add value to BI information. BI data should be:

Real time: or as close to real time as you can get providing a continuous stream of information.

Predictive: in a sense that the insight is used looking forwards, not back.

Business strategy-lead: so that it has business value to maximize profitability or to maintain a certain image—whatever your strategy is, applied to the analytics.

Goal seeking: because the goal is always changing, BI needs to be adaptable, seeking to improve and evolve itself over time.

Actionable for a purpose: not just for general information.

Continuously monitoring product behavior: to better understand that product in relation to store locations and store size profiles.

A shared pool of knowledge: pool knowledge together to be used for collective learning.

Self-improving: must be able to learn because servicing technology defeats the purpose if it involves business user intervention.

Optimizing BI

So now that you have your data, it’s critical to know how to optimize your BI. You can begin with reframing your question: change your business strategy and change the way you look at your problem (i.e. replenishing inventory). Use the insight BI has gathered to predict future demand and then put it in a business context with your strategies to help understand consumer behavior and how it affects business performance. Make sure what you take away from BI is actionable and that you actually deliver results on the answers you gather.

BI is only as good as the questions you ask it. Consider asking new questions: given what you know about your customer, your product, your supply chain, how do you make the most profitable inventory movement and placement decisions, especially when there are at least 5M different decisions to be made every day?

In order to reap the benefits of the results you achieve with BI they must be measurable. If you can’t measure them, how do you know if you were successful? You cannot truly optimize your BI processes unless you can learn and adapt from your mistakes, and imbed them into the execution process, so that when you are faced with a similar situation, you can be certain of the proper way to respond.

Turning BI into automated execution

There are sophisticated new systems that give the user the ability to set up minimum constraints for their product performance that will create alerts when performance falls below those constraints, meaning that they can simplify their time by focusing on the areas of their merchandising process that need attention. The most sophisticated systems available can even use the BI it takes in from SKU/store data, and automatically review the data it needs in order to make decisions and recommendations about what to send, how much to send, and where to send it. This is the most advanced way of integrating BI with inventory execution.

Benefits of BI

BI doesn’t just give retailers the opportunity to realize their problems and create strategies that produce results, but it also leads to a more profitable, better business. BI tools help you sift through the data to see where your real moneymaking opportunities are and give you focus for greater results and better benefits. When BI is executed properly, it leads to significant benefits: increased sales, reduced markdowns, and business growth.

When a retailer can integrate their data with their inventory management processes, they will truly make the most use of both their BI solution and their inventory investment.

Look out for the next blog, on multichannel strategies for retail.

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To read more on BI, check out this post on Busting the BI Myth at: http://quantumretail.com/2010/11/17/busting-the-myths-of-retail-3-business-intelligence-bi

To learn more about Q and it’s constantly learning Qi engine, that links BI with inventory execution, visit: http://quantumretail.com/q-platform/qi-engine

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Watch on Demand: Quantum Retail CEO, Vicki Raport, discusses Retail Decision Analytics at Target’s Analytics Conference

by Quantum Retail • February 10th, 2011 • Write a comment »

Quantum Retail’s CEO, Vicki Raport, was invited to speak at Target Corporation’s Second Annual Retail Analytics Conference on Dec. 16, 2010

In this presentation, Vicki Raport, CEO of Quantum Retail, discusses the concept of Retail Decision Analytics. Going through the history of Business Intelligence (BI) Vicki breaks down where BI is headed today, why analytics need to be actionable, what makes BI successful, and how results-driven transformation can be achieved. The presentation is also followed by an interesting Q&A.

Runtime: 45 mins

Watch on Demand here»

Click here for more information on the conference.

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Busting the Myths of Retail: #3. Business Intelligence (BI)

by Quantum Retail • November 17th, 2010 • Write a comment »

MYTH #3: Business Intelligence (BI) solutions are an effective tool to help execute on real-time demand

TRUTH: In reality, BI solutions alone can help you glean answers from real-time data, but they do not inherently have the ability to execute on those questions, and have a difficult time integrating with your existing systems.

Business Intelligence can be described as the process of enhancing data into information and then into knowledge, from that point, the execution occurs based on what you do with that knowledge. Because of the lack of execution from BI, solutions often fall flat. In fact, “more than half of all BI projects are either never completed or fail to deliver features and benefits that are optimistically agreed on at their outset,” said a spokesman from Atre Group. “There are many reasons for this failure rate: high cost of ownership, lack of ease of use, organizational issues, lack of measurable benefits, benefits restricted to few users, a lack of scalability and so on.” However, there are benefits that can come out of BI, but it must be strategically integrated with retail processes that are driven by merchandising objectives, comprehensively deployed and adopted, and managed in ways that produce meaningful, measurable and credible results.

Too much data

Most retailers have vast amounts of data coming from too many sources. “The theory is that the more you know about your customers and the business problem you’re trying to solve, the better you’re able to solve it,” said Karen Parrish, VP, worldwide sales, BI solutions at IBM. “But by trying to access data from too many sources — data that resides in their own organization, data that resides externally, data that they purchase and bring into the organization, data from the Web and data that sits in e-mail — companies may be shooting themselves in the foot.”

Today one of the biggest challenges retailers face is managing the sheer wealth of data available and selecting what is relevant. “Retailers have always gathered an enormous amount of data, but they don’t always use it very well,” says Jan De Joung, Microsoft’s worldwide retail industry solutions manager. The user must make certain that they are asking the right questions of the data. Instead of looking at how much of a product sold at this time last year, it is more important to look at how much of that product is selling now. When you look at those sales, it is also important to look at not only the maximum sales, but also where you had out of stocks, to understand how much of that product you could have sold. With all of the data available to you, you need to know what information you need to make the best decision, typically this is based on the product’s strategy.

“In the past, BI solutions would tell a retailer some of the facts, such as, ‘you have sold this number of this stock and you made this margin’, but didn’t tell them where they lost margin, in the sense that they didn’t have the right product with the right availability,” says Paul Makin, sales director at K3. “The sophistication of today’s solutions allows people to do far more of that investigation work.”

Real time intelligence

Decisions about short life inventory investment often need to be made months in advance, something that can only be done with access to accurate up-to-the-minute data. “It’s near impossible for any person to get their head around how a decision they’re about to make will effect the entire business,” says Roy Lee of Cognos. “Technology provides an environment where all of the criteria can be entered, the business rules and the business assumptions can be modeled, and those ‘what if’ scenarios can be effectively managed and worked through.” This type of business intelligence, when utilized at the lowest level possible (SKU/store) can immensely increase execution success.

Especially for the fashion sector, accurate and up to date information must be immediately accessible. “The fashion sector is characterised by the frenetic way it has to manage its own business,” says software developer Cesare Dania. “At every trade season, everything starts again and the times are cut drastically. As a consequence, to get information in real time becomes vital.”

Turning intelligence into action

But even with this real time data, if a retailer does not have a strategic and efficient way to act on the data, it’s impact is diminished. However, there are sophisticated new systems that give the user the ability to set up minimum constraints for their product performance that will create alerts when performance falls below those constraints, meaning that they can simplify their time by focusing on the areas of their merchandising process that need attention. The most sophisticated systems available can even use the BI it takes in from SKU/store data, and automatically review the data it needs in order to make decisions and recommendations about what to send, how much to send and where to send it. This is the most advanced way of integrating BI with inventory execution.

Integrating BI into an end to end merchandising solution: Quantum’s approach

BI mixed with strategic merchandising and automation

With Quantum Retail’s system, Q, we combine the business intelligence from product data with automated merchandising processes through a shared pool of knowledge that we call Qi. We follow a strategic approach to merchandising, requiring users to to assign roles and goals to each of their products that will be used to create minimum constraints for every product at every store. Then Qi engine continuously monitors and learns from customer behavior over time, automatically reacting and executing on product objectives to ensure that availability is maintained according to the guidelines set for each product.

Users are not asked to choose the correct data or do the math to meet these objectives, the system does that for you – weighing out the proper execution based on the product strategy, forecast and current demand.

Constant learning

Unlike other solutions, the value and intelligence of Q constantly improves as the Qi engine learns from product behaviors. This means that the value of Q will only increase with time. Item profiles in the Qi engine are constantly updated by the system whenever new data is available, allowing it to accurately predict how that product will act with the intelligence of how it has acted before, while taking into consideration how it is acting now.

Real demand visibility

Q gives you visibility of the real demand in your stores now. With the understanding of lost sales, Q prevents you from missing opportunities so you can capitalize on every potential sale. The Qi engine learns from how your product is moving right now, so you do not need a year’s worth of data to predict how a product will perform. By continuously tracking 35 performance metrics, such as: average demand, average sales, seasonal affects to product life-cycle patterns, shelf life, maximum sales per day, average inventory by date, in stock and in transit, this lets retailers calculate potential consumer activity and demand every day.

Sign up to receive updates throughout the series.

To learn more about Q and it’s constantly learning Qi engine, visit: http://quantumretail.com/q-platform/qi-engine

For more information about BI, check out these On Windows articles:
“6 Tips for Getting the Best out of BI”
“BI Strategy in Retail”

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