This business case deals with the implementation of Retail Matrix. Retail Matrix makes sales intelligence simple. It extracts sales data from the host within user defined time intervals. This data is delivered to the manager’s laptop where the data intelligence is performed.
The case is aimed to assist Retail stores with three strategic challenges of store optimization, resource utilization and shrinkage reduction.
Current impediments to these strategic goals include delivering the right data in a meaningful way to the right people at the right time. Data reporting currently is slow in a fixed format. If managers want to see reporting in different formats specialized reports often have to be requested.
Retail Matrix eliminates these data impediments delivering real sales intelligence as opposed to simple reporting in an interactive capability. Different intelligence views can easily be created on screen by the user.
The immediate benefits include
data time to user, updated daily allowing decisions to be meaningful
specific data to specific users getting people to focus on what they need to do
converting data into intelligence allowing for a more strategic approach to current retail activities
low cost low resource implementation
fast implementation
Introduction
The goal of this business case aligns with the standard requirement of Retailers to provide innovative and value creating solutions to their customer base.
This case is a focused methodology to assist retailers in their quest to optimize the core strategic objectives of store optimization, resource utilization and shrinkage reduction.
The foundation of this business case is driven on flexibility of project scope, implementation speed and targeted goals.
Conditions precedent
The value offer is targeted at Retail stores who have three strategic challenges.
1. Store Optimization. To optimize store performance by having the right merchandise at the right time
2. Resource Utilization. To empower its front line managers with the tools to ensure that their people resources are put to best use.
3. To reduce shrinkage. Through the use of technology, process and policy.
On face value these challenges may be perceived as more operational in nature however they are the key ingredients to the Retail Store competitive landscape.
There are many barriers to these strategic challenges, this business case mainly addresses the “management blind spot” barrier. The proposed solution is to enable the Retail Store manager to move towards evidence based retailing supporting the current “schedule base” retailing.
Common to many retailers, the methodology to replenish, merchandise and mange individual stores are policy or schedule driven. This ensures uniformity and control however it does not easily cater for decision flexibility or opportunity identification.
This is where the blind spot barrier plays a big part. Mangers cannot respond and be proactive if they don’t know there is a problem or opportunity that requires attention.
The goal of this case study is to provide an easy yet effective way for the Retail Store manager to simplify the thousands of different trading variables into meaningful decision profiles enabling the platform for good decisions or at the minimum leading to value driven recommendations.
The methodology to achieve this is through an innovative management tool called Retail Matrix (see www.salesmatrix-usa.com).
Key benefits derived from the Retail Matrix application
• Easy to use. limiting disruption due to lengthy training time requirements
• Flexible deployment. It can focus on the individual store to a group of stores, a planogram across many stores or a product group across many stores.
• Extracts sales data only. No impact to day to day systems with zero data depository risk.
• Targeted intelligence. Different people can have different data sets depending on their needs or job responsibility.
• Interactive. All intelligence is screen driven, eliminating the need for customized reporting and the delay that comes from waiting for these reports.
• Humanize real answers to real life questions. Retail Matrix is real life question driven “where do I make the most money and with what product” or “what is my busiest time of day”
• Customizable. The ability to change behavior by asking specific questions to designated people” This enables forced thinking until behavior changes in the right direction.
• Visual. Making data easy to read
What is Retail Matrix?
Retail Matrix makes sales intelligence simple. It extracts sales data from the host within user defined time intervals. This data is delivered to the manager’s laptop computer where the data intelligence is performed.
The foundation of Retail Matrix is based on three strategic questions
1. Where am I making or losing money?
Enabling focus on the money making segments be it volume, margin or unit driven. Conversely, identifying poor performance segments that may require management attention.
2. Where am I growing?
Placing resources on growth sectors3. What are my opportunities?
Enabling proactive response based on evidence
The secret is in how retail matrix displays its data. The grid or matrix formatted within the two dimensions of rows and columns. The row information can be product driven and the related column dates can be time of day, day of week, weekend weekday, store by store, or specified category driven.
This allows the user to simply right click to change the view based on the related intelligence he or she requires.
Sales performance diagnostics is enhanced through the traffic light system. This process highlights where outliers exist allowing quick prognosis either related to a required action or the decision to not to.
Either way the manger with the Retail Matrix tool has the power to make these decisions based on real evidence.
The value offer
The core strategic areas targeted with the Retail Matrix solution include.
Store optimization. Achieved through
Volume optimization
Margin Optimization
Driven by
Store group comparisons
Product category performance within a single store and across a group of stores
Trend analysis
Price point profiling
Resource utilization achieved through
Volume by time of day
Volume by day of week
Driven by
Unit volumes by product group
Shrinkage reduction achieved through
Volume by time of day
Volume by day of week
Driven by
Unit volume variation by high risk shrink items
Application of the value offer
Each value offer of store optimization, resource utilization and shrinkage reduction is considered within the practical application towards the delivery of the offer
STORE OPTIMIZATION
Store optimization in the context of this case study is defined by the ability to increase the individual store profitability as measured in additional revenue dollars, additional margin dollars or the combination of both. The key measures are revenue growth % and store contribution %. Superior store optimization is the achievement of a profitability growth rate greater that the revenue growth rate.
Achieved through
Volume optimization
Margin Optimization
Driven by
Store group comparisons
Product category performance within a single store and across a group of stores
Trend analysis
Price point profiling
Geographic
By population ethnic concentrations
Rural vs. urban stores
Large vs. small
New vs. older
High rent vs. low rent
Revenue focus strategies can be initiated with the product group by store matrix as shown in figure 1. This screen display is based on revenue dollars but can easily be switched to units with the mouse right click (see figure 2). The user now has the advantage assisted with the traffic light functionality to identify outperformers across all stores. Answering strategic questions including:
Which store/s am I achieving best of breed product performance
Which stores need specific product attention
What is the product variance from one period to the next between the selected stores showing abnormal increases or declines
What is the % contribution to the total revenue by product group by store showing any store with abnormal weightings to any specific product group
Figure 3. Store by store based on time of day trading
Figures 1 and 3 reflect different intelligence profiling which is achieved by right clicking as displayed in figure 2. Figure 2 further illustrates the simplicity of use allowing the same profile to be viewed by margin $ or margin % or by units as the need may arise.
Revenue focus can then be alternated to the time of day view as shown in figure 3 allowing the initiation of various actions focused on optimizing volume during either low or high trading timeframes. This can also be most useful when aligning resources at peak and off peak timeframes.
Figure 4. Click on the question and Retail Matrix automates the answer
Figure 4 allows specific questions to be answered which can be customized depending on the trading or learning requirements.
Revenue and margin optimization strategies can be automated through an easier question to answer driven approach as facilitated in the navigator screen. This navigation capability can be most useful if a process driven approach is required towards the sales intelligence methodology.
First stage implementation can be limited to focus on targeted performance areas that ensure specific attention allocated to certain managers. This targeted approach limits the user’s requirement to identify what they need to look at.
Price pointing allows the manager to determine volumes and margins by different price points. This intelligence capability is most useful when viewed by time of day or day in week.
The key benefits of Sore Optimization include
1. Promotional benefits. The ultimate application here is through the various social networking which such as Twitter and Facebook that enables targeted messages at targeted timeframes mainly reminding people of what they buy and when. We notice that no social marketing programs are being deploys as at today date. Even without the social program RM enhances traditional promotional impact with a more targeted message in specific the ability to make promotions more regionally targeted
2. Margin growth though a higher awareness of the high margin mover and the assurance that they are never out of inventory specifically in the time slots that make those inventory items most popular. (see price point performance)
3. Blind spot growth 1. Simple awareness of where a product or product group is outperforming here in relation to comparative store comparisons. Retail Matrix add to the capability in that it informs as to the different circumstances as to how the outperformance happens for example it may be consistently outperforming on the weekend or at a specific time of day or a specific day of week. How does this help? Numerous controllable factors make create this
a. Different staff gives different results. Who is consistently on duty during the outperformance or vice versa?
b. Store location and ease of access specifically during heavy traffic hours
c. Store location based on its neighbors. Some neighbors are just most lucrative than others. This may not help the underperformer but it will shed a lot of light on new locations.
4. Blind spot growth 2. Representing the Fur coats don’t sell in Florida syndrome. This awareness is manifested in RM through the product sales by store analysis (see figure 1). Here managers can tailor the store merchandise specific for its target market.
5. Simply get rid of poor performers and replace the space with higher performers in specific if this is done on a store by store basis.
RESOURCE UTILIZATION
Store optimization considered the process to maximize revenue and margins. Resource utilization is both cost driven through the reduction of non value added resources and by ensuring that the resources deployed are done so at the most effective manner.
Recognition is also given to the practicalities of resource scheduling. Retail Matrix at a minimum will facilitate where and when would be the best way to align resources within the trading conditions of each individual store.
Achieved through
Volume by time of day
Volume by day of week
Driven by
Unit volumes by product group
The answer to “when do we need more or less hours and why”, can be the difference between normal profit and best of breed store contribution.
The user has the ability to select on screen as to the time of day the most money or throughput is achieved. It is agreed that some of this is obvious it could be stated that in the main volumes are light in the early morning and late evening however consideration should always be made on:
Which stores does this conventional wisdom not apply and what are the peculiar circumstances that create these anomalies
During peak hours what is selling the most and what is the best resource strategy to grow these product sales
When should restocking activities take place and how does this compare to what is actually happening according to the schedule.
Figure 5 Revenue volumes by time of day reflected by geographical profile
Retail Matrix enables the alignment of resources by with store volumes selected on a more global basis as shown in figure 5 or on a store by store basis as shown in figure 6
Figure 6 Store volumes by time of day at store level
This is most useful when looking at an individual store as depicted by figure 7 which will show the volume for a selected store by SKU level (if required) as to when the items are sold.
Figure 7 Volume by time of day for selected store 1317 Broadway
Figure 8. Product categories for all selected stores by time of day
Figure 9 Day of week performance by product category
Figure 7, 8 and 9 illustrate the various intelligence views enabling the practitioner to assess resource requirements for a single store (figure 7) or for multiple stores by product group (figure 8) or by day of week (figure 9). The ability to review performance in this way facilitates high quality due diligence allowing the know before decide core competency achievable.
Figure 10. Time of day by day of week
The benefits here are numerous but to mention a few
1. Cycle counts can be done on items during the time of day when volumes are light.
2. We know which shelves need to be fully stocked when
3. We can match hour paid with volumes
4. We can guide merchandisers from the different vendors where to focus their attention.
5. Where possible we may even change our opening and closing hours.
6. Where possible we may have mobile planograms and strategically place them based on what the shopper buys when.
SHRINKAGE REDUCTION
Achieved through
Volume by time of day
Volume by day of week
Driven by
Unit volume variation by high risk shrink items
Shrinkage, specifically for low value high sought after everyday products is always a retail challenge. Unfortunately the loss may be driven from internally as well as externally. The traditional methods to alleviate shrink include cycle counts to store surveillance techniques. The effectiveness of these techniques varies from excellent to minimal.
The Retail Store is further disadvantaged with its perpetual inventory system which does not easily facilitate accurate cycle counting for example. In addition the lean nature of the Retail Store business does not facilitate adequate resources to resolve potential variance.
Retail Matrix is not an inventory management tool but it does facilitate the ability to change the way shrinkage is managed.
It is a well known fact what is measured is managed or at least prioritized. This measurement visibility often has the same impact as the more extensive deployment of the high cost methodologies as mentioned above.
Retail Matrix has the ability to segment the high risk shrink items and reflect the volumes per high risk as compared to
Similar stores
Same store at different days of week
Variance in performance from one week to the next by item
This focus increases the perception that the items are “watched” on an ongoing daily basis which is often provides more substance as a deterrent.
Retail Matrix data and technology structure
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