| Oracle® Retail Insights User Guide Release 17.0 E95062-02 |
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Metrics (measures) are performance measurements that allow you to analyze business performance. They are usually numeric values. A metric can be as simple as the sum of the values in a fact column, or a highly complex calculation that contains mathematical operators.
A metric can be viewed as a statement that specifies how a performance measure is calculated. The basic component of a metric is a formula that specifies the calculation to be made. A metric can contain other components that specify additional criteria for calculating the metric.
Oracle Retail Insights provides an extensive set of predefined business measures and key performance indicators for business intelligence in a retail environment. You can create your own metrics with the tools available in Oracle BI.
Retail Insights metrics are stored in presentation tables. These tables contain table descriptions that include the level and nature of information provided and the functional area in which the metrics are used. For each metric, the presentation tables contain a description that includes the following:
Metric type, such as count or system metric
Functional area, such as net cost
Definition (for example, base cost is defined as the initial cost before any discounts are applied)
Constraints (for example, net profit data is only available by primary supplier)
In the Oracle BI interface, you can access a summary description of a metric as follows:
Right-click on the metric name.
Select Properties.
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Note: See Appendix B, "Reporting on Oracle BI Repository Objects" for information about producing comprehensive listings of Oracle BI repository objects. |
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Note: See Appendix C, "Retail Insights Metric Definitions" for a complete list of Retail Insights metrics. |
Comparable (comp) stores are stores that are open for business for a set period of time and were in operation within the time period of analysis. In other words, comp stores are really established stores as opposed to new or closed stores. Comp store measurements are important to an analyst because profits and sales from the more established stores provide stable indicators of business performance. New or closed stores tend to be more volatile and can have a skewing effect on business performance indicators. Sales and profits from new or closed stores are not really comparable in business analysis, and as a result, they are not included in the comp store measurements.
The Comparable Store Flag can be sent either from the retailer's merchandising source system or can be derived at in RI by utilizing a mathematical formula based on the store open date to determine if a store is comp or not. Regardless of the method used to determine comparable store status, the Comp Flag attribute is provided as a means of reporting metrics based on the comp status of a location. Additionally, special Comp metrics are provided in multiple subject areas to support side-by-side reporting of comp data with other metrics. If using the Same Store type of reporting, the Comp Anchor Year attribute must be used to specify which year the comp flags will be taken from. For example, if the reporting period is March 2017, then a filter must be set such that Comp Anchor Year = 2017.
The Comp Store Measurements measure the growth in sales and profit, excluding the impact of newly opened stores. Sales and profits from new stores are not reflected in same-store comparisons until those stores are converted to comparable stores. With this approach, stores whose open dates are not captured in the source system are not included in these comparisons. Each store needs to have a store open date as well as a store close date when a store is closed. If there is no close date, the store is assumed to be still in operation.
Cost and profit analysis helps a retailer to understand the financial impact of various business decisions such as:
Stock levels for high-profitability items
Deals negotiation for low-profitability items
Promotions worthiness
Profit is calculated as the difference between sales amount and cost of the item in the transaction. The cost of the item in the sales transaction is based on the weighted average cost of that item in the merchandising system at the end of the day.
Net cost (sometimes called deal cost) measures are held at the supplier level.
Net cost is populated with data from Oracle Retail Merchandising System (RMS) or another source system. The data from RMS consists of cost values that represent different discounts on base cost that the supplier provides. These discounts can include the following:
Deals with deal partners for items, or items at specific locations
Deal partners can be suppliers, wholesalers, distributors, and manufacturers. Within a deal, you create deal components, specify the items for the deal component, and define thresholds.
Fixed deals with suppliers
Your organization receives payments from suppliers in return for mentioning their products in promotions, or for displaying their products on prime shelf space.
Bracket costing deals with suppliers
Your organization receives a certain deal price on an order, depending on the size of the order. Different types of brackets can be established based on mass, volume, pallet, case, each, or stat case.
Retailers plan markdown strategies carefully, as they make pricing decisions for their products with an eye toward keeping inventories at optimal levels, while driving gross margin revenue across key areas of the merchandise hierarchy.
Retail Insights markdown analysis allows reporting on a range of data related to markdowns and markups. These include permanent and point-of-sale markdowns and markups, as well as markup and markdown cancellations, at granularities of item, location, day, and retail type (regular, promotion, and clearance).
A buyer planning a promotion strategy for a category of goods might be interested in promotional markdown totals in a certain department, year to date. On the other hand, a finance executive might want to analyze clearance markdown amounts compared to promotional markdown amounts at the corporate level, on the same report with profit comparisons from clearance versus promotional sales.
A sales forecast is a calculation of the potential sales of an item for a future period, based on past performance of the product. Sales forecast analysis helps a retailer to develop a marketing budget, allocate resources, and get a early sense of deviations from financial goals. Sales forecast analysis also helps the retailer to determine the effectiveness of forecasting techniques.
Retail Insights stores sales forecast data at the item-location-week level. The sales forecast quantities exclude value-added tax (VAT). Retail Insights data model is extended to add forecast sales amount and forecast sales margin amount into sales forecast, in order to support the Science engine advance clustering calculations.
Inventory Adjustments are changes to inventory level in units, retail and cost value. Inventory Adjustment analysis provides visibility to Inventory analysts, Inventory controllers, Inventory managers, Category managers and store managers for analyzing the reasons and plan accordingly to overcome the potential problems that are causing the stock adjustments. Inventory Adjustments impact the stock ledger as the inventory value for a location is impacted. Ending stock value will be increase/decreased making the stock as over-valued or de-valued.
Retail Insights holds the inventory adjustment units and value by reason code at item, location and day level.
Inventory transfer is the movement of stock between the retailer's locations. Inventory transfers analysis will help the retailer in taking appropriate and profitable decisions to improve the sales by initiating stock transfer from nearby locations to avoid lost sales.
Retail Insights supports three types of transfers, normal, book and intercompany, with an attribute called transfer type.
Book transfer items are inventory units moved from one part of the retailer's location to the virtual location.
Normal transfers are the inventory moved between the retailer's physical locations. (Store or warehouse)
Intercompany transfer items are inventory units moved from one legal entity into another legal entity. RI holds transfer units and cost and retail values of transferred units.
Inventory transfers are held at the item or subclass, destination (to) location, shipping (from) location, and day or week levels. When you want to see total inventory transferred to a location (with no regard for the From Location) then simply use the standard Organization attributes, such as Loc Number. If you want to see the movement of inventory between locations, then you must use the From Organization dimension as well as the Organization dimension.
Inventory receipts are units purchased and placed in inventory or units received from other retailer locations as part of a transfer or an allocation. Inventory receipts analysis provides visibility to and control of your accrued liabilities for inventory items. Inventory receipts transactions are recorded in the general ledger at the time of receipts.
Retail Insights holds the number of units received at the day and week level, at both retail and cost value. Inventory receipts are held at item level for day and week and at subclass (segment) level for day and week.
Receipts are also differentiated by type, based on whether they are the result of a purchase order, allocation, or non-allocation transfer. A Transaction Code dimension is provided to separate inventory receipts by type, as well as metrics to allow reporting on specific types of receipts.
Unavailable inventory is on-hand inventory which is currently in a non-sellable state, such as damaged goods. Such inventory is still considered part of a location's total on-hand inventory, but it cannot be sold to customers. Unavailable inventory analysis provides visibility into the types and amounts of non-sellable goods currently being held at a location.
Retail Insights maintains a record of unavailable units and the value of unavailable units in cost and retail amount, grouped by status code, item, location, and day.
RTV units are units returned to the vendor for any reason (overstock, poor quality, etc.). Return to vendor analysis gives retailer valuable insights for evaluating vendor performance.
Retail Insights maintains record of RTV units and the value of RTV units in cost and retail amount. RTV facts are held at the item/supplier/location/day/return reason level.
Sales reporting helps the merchandising executive to identify sales key performance indicators and determine the operational effectiveness of sales, to evaluate whether sales achieve the results set during sales planning. This can help sales managers to take timely corrective actions when they see deviations from projected values.
Gross sales value is the total amount the retailer sells to consumers. Gross sales value is calculated by multiplying the unit price of an item by the number sold to consumers. Returns are the portion of sales that are returned to the store for a refund. Sales value is the net value after customer returns are subtracted from gross sales value.
Retail Insights maintains gross sales and returns for amounts and numbers of units in separate fact columns. Separation of these values allows analysis of returns and the use of gross sales in calculations where this is desirable. Net sales value is required for most calculations.
In addition, the retailer may need to track sales according to price type to allow analysis of sales for promotional and clearance items. Retail Insights holds sales amount and units by retail price type to allow analysis at this level.
A sales pack is a group of individual items grouped together by the retailer to be sold as one item. An example is a bottle of shampoo and a bottle of conditioner, both individual items on their own, but packaged together to be sold as a unique pack item.
Retailers require visibility to pack sales contribution information by regular, clearance, and promotion retail types. This analysis provides the ability to compare and contrast location performance of pack sales using retail type measures.
These metrics can help to determine:
How a SKU sold as a single item
How the pack itself has sold historically
How a SKU sold when it was included in a specific pack
Retail Insights extraction, transformation, and loading processing prorates the value of a pack into its component items (see "Prorating of Packs" later in this section). This helps in analysis of component pack item contribution to pack sales.
Prorating of Packs
The prorating of a pack's value into its component items requires calculation. The following formulas are used for prorating packs:
Item Prorated Sales Value = Pack Sales Value * Item Prorate % Item Prorate % = (Item Price * Pack Item Qty) / Pack Component Sales Value Pack Component Sales Value = (Item A Price * Item A Qty) + (Item B Price * Item B Qty) + (Item C Price * Item C Qty) + …+ (Item n Price * Item n Qty)
Example
Pack A has a pack sales value of $90,000. Each pack is priced at $9 and contains the following:
Calculation Steps
Calculate pack component sales value:
Item A Price * Quantity of Item A in Pack A
4 * 2 = 8
Item B Price * Quantity of Item B in Pack A
2 * 1 = 2
Item C Price * Quantity of Item C in Pack A
1 * 1 = 1
8 + 2 + 1 = 11
Calculate item prorate percent:
8/11 = .7273 (Item A)
2/11 = .1818 (Item B)
1/11 = .0909 (Item C)
Calculate item prorated sales value:
$90,000 * .7273 = $65,457.00 = Item A Prorated Sales Value
$90,000 * .1818 = $16,362.00 = Item B Prorated Sales Value
$90,000 * .0909 = $8,181.00 = Item C Prorated Sales Value
Supplier invoice reporting can help retailers achieve control of a supplier's payment process and assess the discrepancies for a supplier.
Supplier invoice cost is the actual cost as shown on the supplier invoice (from Oracle Retail Invoice Matching or other source system). Supplier invoice purchase order cost is the expected cost previously agreed upon in the purchase order, before any deals or discounts. A difference between the two can reflect deals, discounts, clerical errors, or dishonesty.
Supplier invoice cost and supplier invoice purchase order cost are held at the supplier-item-location-day level.
The merchandising organization must carefully select, monitor, and adjust relationships with suppliers. Before negotiations with suppliers, the retailer can prepare by running supplier performance and compliance reports.
Supplier performance considers typical merchandising measures such as net sales, profit/margin, markups, and return rates, to compare the profitability and inventory costs of goods provided by different primary suppliers.
Supplier compliance measures allow buyers to assess supplier delivery timeliness and purchase order fill rates. For example, how many advance shipping notices came in early, on time, and late? Were overall purchase order counts at expected levels, under, or over?
This analysis can help the retailer to negotiate supplier-funded promotion negotiations and supplier bill-backs, and reward responsive and flexible suppliers. This in turn can reduce inventory costs, prevent out-of-stock conditions, and increase profitability.
This functional area focuses on reporting that provides supplier performance information based on key performance indicators. Collection of this data makes the following types of analyses possible:
Compare and contrast supplier performance over time
Compare and contrast department performance by primary supplier
Monitor department performance in terms of sales volume and value
Compare and contrast market supplier with supplier performance
Department managers in particular need to understand sales and profit contribution information about their suppliers. Retailers can monitor supplier performance better by identifying suppliers of profitable items, measuring contributions to total department performance, and identifying how categories are performing relative to other categories, and relative to last year.
Unless facts (such as net cost) are stored by supplier, all facts in that data can only be attributed to the primary supplier.
The following types of measures are a part of supplier performance:
Sales and profit
Sales value and variance in sales value from last year
Sales units and variance in sales units from last year
Profit amount and variance in percent profit from last year
Percent contribution to total sales value for the department
Inventory position and movement
Sell-through
Stock turns
Beginning stock on hand (BOH) and ending stock on hand (EOH) retail value
Receipts
Gross margin return per dollar of inventory (GMROI)
Net (deal) cost
Net cost (sometimes referred to as deal cost) measures are held at the supplier level. Net cost is populated with data from Oracle Retail Merchandising System (RMS) or another source system. The data consists of cost values that represent different discounts on base cost that the supplier provides. These discounts may be:
Deals with deal partners for items, or items at specific locations
Deal partners can be suppliers, wholesalers, distributors, and manufacturers. Within a deal, you create deal components, specify the items for the deal component, and define thresholds.
Fixed deals with suppliers
Your organization receives payments from suppliers in return for mentioning their products in promotions or for displaying their products on prime shelf space.
Bracket costing deals with suppliers
Your organization receives a certain deal price on an order, depending on the size of the order. Different types of brackets can be established based on mass, volume, pallet, case, each, or stat case.
Supplier compliance measures supplier performance based on key performance indicators such as timeliness and accuracy of deliveries. The supplier compliance functionality supports supplier evaluation based on the following parameters:
Timeliness
Delivery accuracy
Order fulfillment
Supplier invoice cost is the actual cost as shown on the supplier invoice (from Oracle Retail Invoice Matching or other application). Supplier invoice purchase order cost is the expected cost previously agreed upon in the purchase order, before any deals or discounts. A difference between the two can be reflective of deals, discounts, clerical errors, or dishonesty.
Supplier invoice cost and supplier invoice purchase order cost are held at the supplier-item-location-day level.
Retail Insights supplier compliance data provides the ability to report receipt units grouped by supplier, item, location, and day. For example, the fact column RECEIVED_QTY contains the quantity from the qty_received column in the RMS SHIPSKU table.
The supplier compliance data does not contain cost or sales data, so it cannot be used to report sales or cost by supplier. The quantity in the supplier compliance data should not be confused with receipt units in the inventory movement data.
Timeliness measures the supplier's ability to deliver according to schedule. Early, late, and on-time shipments are tracked in the supplier compliance area. You can measure supplier timeliness on a daily basis.
Timeliness = No of On Time Deliveries / ( No of On Time Deliveries + No of Early Deliveries + No of Late Deliveries )
For example, if the number of on-time deliveries is 75 and the total of all deliveries is 100, the timeliness rating is 75 percent.
Missed deliveries are deliveries that did not take place within the time frame specified. A late delivery is also a missed delivery. Because the timeliness measure would not be meaningful if two of its components were counted twice, missed deliveries are not included in the timeliness measure. Missed deliveries can be reported at the supplier-location-time level as a separate metric.
Delivery accuracy measures the supplier's ability to deliver the correct items and quantities on the order. The rating is determined by comparing the total number of deliveries for the supplier to the number of deliveries where the quantity or item was incorrect.
Delivery Accuracy = Number of ASN Expected Deliveries / Number of Deliveries
where:
Number of Deliveries = No of ASN Expected Deliveries + No of ASN Over Deliveries + No of ASN Under Deliveries + No of Mismatched Deliveries
A mismatched delivery is a delivery that contains at least one mismatched item.
For example, if the number of on-time deliveries is 75 and the total number of deliveries is 100, the delivery accuracy rating is 75 percent.
Order fulfillment measures the supplier's ability to deliver on order in full. The rating is determined by calculating the ratio of completely filled order to the total number of orders.
Order Fulfillment = No of Full Order Deliveries/ Total Orders
where:
Total Orders = Orders Received in Full + Orders Received in Part + Orders Received in Excess
For example, a supplier earns an order fulfillment rating of 75 percent if the total number of orders is 4 and the number of partial deliveries is 1.
Retail Insights holds stock position at a very low level, which is the ending position for every day for every item at every stockholding location. The available stock position measures include quantity, retail value, and cost amount (usually interfaced from source systems based on weighted average cost calculation).
There are three distinct groupings of stock position in Retail Insights:
On-hand stock (goods owned by the retailer and received in a location)
In-transit stock (goods owned by the retailer, received into one location such as a distribution center, but currently in transit to another store or warehouse)
On-order stock (goods on an approved Purchase Order which have not yet been received)
Two examples of on-hand measures are ending on-hand (EOH) for a time period, as well as beginning on-hand (BOH) for a time period. The EOH position for week 1 is the BOH position for week 2.
Stock position is a constant state in which a value or position shifts over time. Stock on hand is at a certain position at the beginning and end of a week and at any point between. Positional values cannot be added together to arrive at a meaningful number. For example, the ending stock-on-hand values for the days in a week do not add up to the ending value for a week. Rather, there is a position at the end of each day and, in this example, the ending position for the week is the same as the position for the last day of the week. For this reason, positional measurements are semi-additive. They are not additive in the time dimension. In other dimensions, they act much like transactions. For example, the ending on-hand value for a subclass can be determined by adding the ending on-hand values for all items in that subclass.
Comparing ending inventory value to the same period last year is a typical scorecard measure, but deeper analysis and more complex calculations are also required. Retail Insights offers critical inventory calculations such as gross margin return on investment (GMROI), weeks of supply, stock turnover, sell-through, and the critical out of stock percentage measures.
A buyer might use one of these calculations to pair net sales and net profit measures on the same report with the out-of-stock percentage for the current month, to assess whether a certain department had low sales performance because of stock unavailability.
An inventory analyst can track the inventory age of existing inventory at a given location. The movement of merchandise from a warehouse to the stores in a timely manner is critical to business. Merchandise lying in a warehouse for a long time adds to the expenses and also brings down profits. Inventory aging related metrics will provide the basis for calculating the inventory age, amount value and the percentage of inventory that has aged beyond a certain set time period.
Some of the questions that can be answered as part of the Inventory aging analyses are:
What is Quantity/Cost/Retail of received merchandise that is still present at the given location beyond a given time period?
How quickly is the Merchandise being distributed from the servicing DC to the stores?
What percentage of merchandise is aging at the servicing DC and for how long?
What is the trend of inventory aging this year compared to last year?
Wholesale metrics enable reporting on wholesale transactions as distinct from regular retail transactions, allowing retailers to understand how their wholesale business is working as a stand-alone operation. This will keep the wholesale business from being lost in the noise of their overall sales. The list below is unique wholesale metrics, but also all the regular sales metrics can be used to do wholesale analysis by filtering for transactions at wholesale locations.
Oracle Retail Insights has three types of franchise metrics: Stockholding Franchise, Non-Stockholding Franchise, and Franchise. Which one a retailer uses will depend on their relationship with their franchise locations: if they manage inventory and replenishment for their franchisees, then Stockholding Franchise metrics are more useful, but if their franchisees operate relatively independently, Non-Stockholding Franchise metrics would be appropriate. Markdown and Markup metrics are simply known as Franchise metrics because there is no way to distinguish between stockholding and non-stockholding for this type of metric.
Consumer analysis is a method by which retailers will analyze their target consumers in order to determine the most effective strategies to improve both their sales and profitability. This analysis can be done by consumer, date, trade areas, consumer segments, historical performance, price strategies, promotions, clearances, demographics and loyalty programs.
Pricing analytics can help retailers determine the optimal pricing of products. It focuses on the proposed pricing of merchandise. Cost elements and profit components are not evaluated as part of pricing.
Retail Insights holds price as a retail value for an item, day, and location. For the purpose of analysis, the average price is calculated over the time period selected for the report. The basic Price metric is intended for use at the lowest level of available data, while the Average Price metric can be used at higher levels of aggregation where you need to see the current average price.
Retail Insights holds facts for both preseason (original) and in-season (current) planning in several reporting areas, including sales, markdowns, receipts, inventory, gross margin, and open-to-buy, in both dollars and units. RI stores planning data at intersections of Merchandise Hierarchy, Organization Hierarchy, and Time Hierarchy. The Merchandise Hierarchy includes item (including style), subclass, class, department, group, and division. The Organization Hierarchy includes store, channel, and company. The Time Hierarchy includes day, week, period, quarter, and year.
RI provides up to 3 possible combinations of intersection of the 3 hierarchies per each implementation. The 3 possible combina-tions can be configured during the installation time to decide which level of Merchandise Hierarchy, Organization Hierarchy, and Calendar Hierarchy will be used.
The following abbreviations are used in the names of Planning metrics:
CPC: Current plan for cost-based planning
CPR: Current plan for retail-based planning
OPC: Original plan for cost-based planning
OPR: Original plan for retail-based planning
Retail Insights information for stock ledger analysis comes from Oracle Retail Merchandising System (RMS).
The lowest-level stock ledger facts are kept at the subclass and week level. This gives Retail Insights visibility to store/subclass/week level and subclass/month level. Stock ledger reporting is not available at the item and day levels. Reports and drills into data that are lower than the subclass/week level return null values for stock ledger facts.
If you receive stock ledger information from RMS, the RMS stock ledger feed to Retail Insights supports either a 4-5-4 fiscal calendar or Gregorian calendar.
If you have a Gregorian stock ledger, reporting in Retail Insights can be done at the subclass, location, and month levels. Reports and drills into data that are lower than the subclass/month level return null values for stock ledger facts.
If you have a 4-5-4 stock ledger, you can analyze the stock ledger at the subclass, location, week, and month levels. Reports and drills into data that are lower than the subclass/week level return null values for stock ledger facts.
Any other calendars, such as a 13-period time calendar, are not supported by the RMS interface to Retail Insights for stock ledger facts. If an RMS user customizes the stock ledger to use a 13-period calendar, there are inconsistencies with the RMS stock ledger interface to Retail Insights unless modifications are made.
Because the month-level stock ledger is directly related to the RMS MONTH_DATA table, data for a specific month is available in Retail Insights after the close of that month.
Baseline metrics are derived from data mined during a period of time when an item is not on promotion.
The baseline process brings sales transaction data from Retail Insights into a suitable structure for performing baseline calculations. The process first transfers sales data by week, identifying which weeks are suitable to be included for baseline calculation. A set of item/location weekly sales is suitable for baseline calculation only if it does not have promotion sales for the week. The number of weeks of sales data to use for baseline calculation is configurable, with a default suggested value of 16 weeks, eight weeks prior to the promotional week and eight weeks after. You can configure both the number of weeks included and whether they are pre-promotion or post-promotion weeks. For example, 14 weeks might be included in the calculation, with eight weeks pre-promotion and six weeks post-promotion. After processing, the calculated baseline metrics are returned to Retail Insights.
These metrics are calculated at the promotion component/item/location/week level. They include baseline units, sales, profit, and transactions.
Baseline metrics can be used by a buyer during category planning, to establish expected sales for a category before promotions are added. This can help identify the level of promotion needed for the category to hit sales targets. A planner might decide that sales goals can be reached without promotions, or by promoting very little, thus saving money and adding to category margins.
Baseline metrics can also be used to calculate lift for promoted products; that is, how much over the baseline did sales increase when this category was promoted? If the difference between baseline and promoted weeks is large, and baseline sales are unacceptably low, it might be concluded that customers are shopping the category only for promoted items. Promotions might need to be cut back or changed, to avoid conditioning customers to buy items only when they are on promotion. If the difference is too small, the promotions might not be effective and not worth the cost to run them.
Retailers want to analyze the impact of new item introductions, and item promotions, to see whether customers come back a second and third time after trying something new. Something new may be a new item introduction, or the first time an item is put on promotion, perhaps as part of raising that item (or Brand's) profile, and so on. Trial and Repeat Metrics can help to analyze the repeat purchase behavior of customer household for the merchandise.
Market Basket Analysis reports can be used to understand what sells with what, including probability and profitability of market baskets. Such reports can be used to shape promotions, optimize product placement and support store planogram decisions. These metrics can help you understand the statistical relationship between sales of different merchandise.
The Customer Insights module enables you to perform retail analysis around customers and customer segments. The following are some example business questions that Customer metrics can help to answer:
Who are my most profitable customers? Who are my most frequent shoppers?
Are my customers only buying items from me when they are on promotion?
What does a customer buy from me on a typical shopping trip? Does it vary by where they live or how much money they make?
Which of my departments appeal to which of my customers? That is, who is shopping in my stores and what are they shopping for?
Retail Insights has a number of metrics against which to measure a promotional sales, cost and forecast as well as Promotion Campaign costs. These metric provide useful insight into the processes of managing actual marketing cost, evaluating financial performance of marketing tactics, and analyzing forecast and actual spending.
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Note: Promotion Budget only supports as-is reporting. |
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Note: Amount facts are in local and primary currency only. |
A cluster is a group of stores. Retailers make store clusters for various reasons, but the general idea is that stores in a cluster should have some key element or elements in common, which differentiates them from stores in other clusters. These elements could involve business objectives like store performance benchmarking, inventory management, and assortment/space planning. Then clusters can be used for analysis of sales, inventory, and promotions. Performance, inventory, ranging, trade area analysis, and union analysis are examples of elements around which clusters are built.
Oracle Retail Insights' cluster metrics enable retailers to analyze their clusters' sales, inventory position, inventory receipts and promotions, so that any analysis that might normally be limited to some aspect of the organizational hierarchy can instead be performed on a retailers' customized store cluster, enabling precise, actionable analysis.
Customer orders lie at the heart of the modern retail experience. Virtually every customer transaction that takes place outside of a brick-and-mortar store is captured as part of a customer order, whether it is a normal sale, cancellation, return or exchange. A customer order consists of a customer order header that contains one or more customer order lines. Oracle Retail Insights' customer order metrics allow retailers the flexibility to analyze the performance of their business across the various selling channels their customers use.
Retail Insights supports a number of different metrics related to customer orders to allow performance analyses of omnichannel retailing. A list of the major metrics (minus the time transformations such as LY and LW) is below.
Similarities calculate how likely a customer is to switch from one item to another in a range from 0 to 1. For example, if the similarity rate for Toothpaste A and Toothpaste B is 0.75 while the similarity rate for Toothpaste A and Toothpaste C is 0.21, the customer is more likely to switch to Toothpaste B than Toothpaste C.
A competitor is a retailer with a product range and customer base similar to those for the organization business unit [Store location in RI] and its channels. The competitor entity holds information about each competitor store and associates it with a location in the organization. Competitor pricing details can be associated with a specific competitor location and mapped to an item in the product hierarchy. This structure provides the means to compare competitor prices for similar or identical items, at a direct competitor location. With this type of timely information, promotion and pricing strategies can be implemented by retailers to prevent potentially costly customer defections.
Purchase orders and pre distribution of merchandise that is on the purchase order is instrumental to a retailers inventory movement. Analyzing various aspects of merchandise that is currently on order i.e merchandise that is on an approved purchase order where the entire quantity has not yet been received is important as it can give insight into the quantity, value and status of the merchandise that will be incoming in the near future.
A key metric that the retailers would track is the on order merchandise quantity that has been pre distributed so that the merchandise reaches the stores via an allocation without any delay.
Some of the questions that can be answered as part of the On Order analyses are:
What is the Merchandise that is on order in terms of Units/Retail/Cost by Supplier/Purchase Order/Item/location/day?
What is quantity/Cost/Retail of ordered merchandise that has been received from supplier by Purchase Order/Item/location/day?
What is the quantity/Cost/Retail of the ordered merchandise that is yet to be shipped by the vendor by Item/location/day?
Oracle Retail Insights Purchase On Order metrics help identify the on order, total ordered, received, cancelled merchandise quantity and value and the allocated quantity and allocated percentage of the PO on order qty.
Gift cards are prepaid, stored-value money cards issued by retailers to be used instead of money for purchases. Gift cards are important for retailers because they drive foot traffic and sales, and it would be valuable for them to be able to quantify that effect and any trends up or down that could be an issue. If gift card purchases and redemptions are not up to expectations, retailers may need to take steps like consumer education, or adding mobile platform gift cards.
Oracle Retail Insights gift card metrics provide analysis on gift card amount sold and the trend with respect to last year.
Transaction tender identifies the tender types that have been used to pay during a given sale or return transaction. This can be used in customer segmentation analysis, even in absence of a customer loyalty program. Unique customers can be identified by their encrypted credit card numbers and their purchase histories tracked and aggregated. Transaction tender data can be utilized to generate gift card redemption analysis.
Oracle Retail Insights transaction tender metrics provide analysis on tender amounts per tender type, gift card redemption amount and the trend with respect to last year.
Sales Discount lists the various discounts that were applied for a given sales transaction. Analysis can be done on the discount amount, discount type and coupon discounts applied.
Oracle Retail Insights sales discount metrics can form a basis for analysis of coupon sales penetration that can help retailers understand if the cost of producing and distributing coupons is worthwhile.
Store traffic information is used to understand the distribution of traffic by minute, hour, day of the week, store location, seasonal periods, promotion periods, total chain, etc. Retailers can also look at the conversion ratio of their store which is the total sales transactions divided by total traffic. You will be able to determine if your conversions went up, down or remained the same during the promotion.
Oracle Retail Insights store traffic metrics can be used to analyze the store traffic and conversion rate of stores in comparison to comparable stores. Traffic data is loaded and viewed in 24-hour time format, ranging from 0000 to 2359.
Customer loyalty activity refers to transactions which involve a retailer's loyalty programs, such as loyalty point accrual, redemption, expiration, and award generation. This information can be used to analyze how customers are interacting with your loyalty program and how effective the program's benefits are. For example, if customers are accruing a large number of loyalty points through sales transactions but are not redeeming them, it could indicate that the program's rewards are not enticing enough to encourage participation. It is also useful to know how many loyalty points have been issued but not redeemed, as these represent a potential liability for the retailer in terms of future discounts and coupons that may be used to purchase products.
Retail Insights loyalty activity can be extracted from Customer Engagement, and is held by program, account, customer, location, and day.
Loyalty award activity tracks the distribution, redemption, value, and expiration of loyalty awards issued to a customer. Loyalty awards usually come in the form of e-awards or entitlement deals that are distributed to customers who have accumulated a certain number of points as part of a Loyalty Program. The generation and distribution of loyalty awards are done via scheduled jobs in Customer Engagement. The rules determining the award type, award frequency, award amount and the number of points that will be subtracted from the customer's account are defined in the award rules linked to a loyalty program level (rules are not extracted from CE to RI).
Retail Insights loyalty award activity can be extracted from Customer Engagement, and is held by program, account, customer, and day.
The following chart shows Retail Insights metric metadata. Users should be aware that you cannot mix facts across as-is, as-was, and point-in-time subject areas.
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Note: Performance of reports that contain YTD metrics may become less optimal as the end of the fiscal year approaches, due to the increasing amount of data that accumulates. Users should be aware of this and take steps to mitigate any performance effects, such as being specific with filters and prompts to get back the smallest amount of data necessary for analysis. |
Table 6-2 Metric Metadata
| Merchandise Insights | Customer Insights | Metrics | As-Is | As-Was |
|---|---|---|---|---|
|
X |
Cost and Profit |
X |
X |
|
|
X |
Markdowns and Markups |
X |
X |
|
|
X |
Sales Forecast |
X |
X |
|
|
X |
Inventory Receipts |
X |
X |
|
|
X |
X |
Sales |
X |
X |
|
X |
Sales Discount |
X |
X |
|
|
X |
Transaction Tender |
X |
X |
|
|
X |
Gift Card Sales |
X |
X |
|
|
X |
Store Traffic |
X |
X |
|
|
X |
Competitor Pricing |
X |
X |
|
|
X |
Sales Pack |
X |
X |
|
|
X |
Supplier Invoice |
X |
X |
|
|
X |
Supplier Performance and Compliance |
X |
X |
|
|
X |
Inventory Position |
X |
X |
|
|
X |
Wholesale |
X |
X |
|
|
X |
Franchise |
X |
X |
|
|
X |
Price |
X |
X |
|
|
X |
Planning |
X |
X |
|
|
X |
Stock Ledger |
X |
||
|
X |
Trial and Repeat |
X |
X |
|
|
X |
Sales Promotion |
X |
X |
|
|
X |
Customer Order |
X |
X |
|
|
X |
Customer Order Promotion Transaction |
X |
X |
|
|
X |
Customer Order Status Fact |
X |
X |
|
|
X |
Customer Order Transaction |
X |
X |
|
|
X |
Touch Point |
X |
X |
|
|
X |
Retail Promotion Actuals |
X |
X |
|
|
X |
Retail Promotion Forecast |
X |
X |
|
|
X |
Promotion Baseline |
X |
X |
|
|
X |
Promotion Budget |
X |
X |
|
|
X |
Consumer Spend |
X |
X |
|
|
X |
Sales Promotion |
X |
X |
|
|
X |
Inventory Position |
X |
||
|
X |
Return to Vendor |
X |
X |
|
|
X |
Inventory Adjustment |
X |
X |
|
|
X |
Inventory Transfers |
X |
X |
|
|
X |
Similarity Score |
X |
X |
|
|
X |
Purchase On Order |
X |
X |
|
|
X |
Customer Loyalty Activity |
X |
||
|
X |
Customer Loyalty Award Activity |
X |
