DiAna: Digital Analytics Pro

DiAna: Digital Analytics Pro - Analytical Class BI OLAP System

Specialized in solving retail problems, the program helps to make informed management decisions based on the analysis of sales and dynamics of inventory: optimize the assortment and inventory, correctly determine the profile of a typical store buyer and his preferences, determine the optimal prices that will help increase sales. Such capabilities are especially in demand when processing large amounts of data, when it is very important not to miss the logical thread of reflection due to expectation. Thus, the formation of a schedule of the dynamics of sales or inventory of a large trading enterprise for the year takes only a few seconds.

The DiAna: Digital Analytics Pro system makes the use of adapted mathematical models to solve applied retail business analysis problems. Among them, the Boston Matrix, Dibb-Simkin methods, econometric models, etc. modified for assortment analysis and planning.

DiAna: Digital Analytics Pro is one of the few systems designed specifically to analyze the information of each individual check, sales at each individual checkout, up to and including an analysis of the workload of a particular change of cashiers on it. This approach makes it possible to draw up a full-fledged portrait of the buyer, develop positioning taking into account his needs, up to taking into account the time of year, time of day and geographical location of the outlet and on the basis of this to form a well-thought-out assortment and pricing policy.

2018: Transition to In-Memory technology

France Informatique & Technologie (FIT) in February 2018 announced the transfer of its flagship development - the DiAna: Digital Analytics Pro system specialized in solving analytical problems of network retail - to the advanced In-Memory technology.

In-Memory technology enables high-performance performance with ever-increasing amounts of data. DiAna: Digital Analytics Pro as a Visual Data Discovery class solution offers users an interactive graphical user interface, which corresponds to the demand of modern retail for convenient, visually simple and fast BI systems for solving data mining problems.

Due to the fact that the In-Memory version of the DiAna: Digital Analytics Pro system, is focused on the use of relatively inexpensive servers and several orders of magnitude higher data processing speed, a retailer of all sizes can now quickly and simply increase the efficiency of managing their business, constantly monitoring information about who, where, when and what goods buys in his trading network, tracking positive and negative trends, building a balanced assortment, improving inventory management, and more.

DiAna: Digital Analytics Pro is one of the systems designed specifically to analyze the information of each individual check. This approach makes it possible to draw up a full-fledged portrait of the buyer, develop positioning taking into account his needs, up to taking into account the time of year, time of day and geographical location of the outlet and on the basis of this to form a well-thought-out assortment and pricing policy.

The DiAna: Digital Analytics Pro system allows you to use mathematical models adapted to network retail problems to solve applied business analysis problems. Among them, the Boston Matrix, Dibb-Simkin methods, econometric models, etc. modified for assortment analysis and planning.

Another feature of the system is its versatility in docking. The presented version of DiAna: Digital Analytics Pro, like the previous one, can be quickly integrated with any other information system used by retail companies to manage commodity movement and cash operations.

A distinctive feature (our know-how) of the DiAna: Digital Analytics Pro analytical system developed by FIT is the solution of applied analytical problems of different classes of complexity in relation to network retail, linked to the processing of large amounts of sales data. If many BI-class solution providers mainly solve data visualization problems, then we have implemented a combination of visualization and analytical processing (application know-how) of data when solving the problems of managing supply chains of network retail. This makes it possible to see not just an abstract, albeit visual, representation of data, massively used for all other industries, for any business, but to get the results of in-depth analysis based on expert knowledge of the industry characteristics of network retail. This is not just help in improving the efficiency of the retail trading business, but also a tool for creating prerequisites for creating competitive advantages that allow you to switch to a different level of business management quality and become a so-called analytical competitor, "said Vladimir Novikov, technical director of France Informatique & Technologie (FIT).

What dictated the feasibility of using specialized analytical systems for retail?

The financial crisis and the aggravation of competition in Russian retail dictate the need to find internal reserves. The period of the race for the growth of capitalization is replaced by the time of increasing the operational efficiency of retail enterprises. A new ubiquitous trend is the increasing role of analytical information systems in solving such problems.

Retail trading companies managed to accumulate so much historical data on their operating activities, which, on the one hand, is already enough for reliable statistical consideration, and on the other , it is critically large for storage and analysis within the transactional system. They require more and more time and resources to process them. Therefore, it is almost impossible to regularly and quickly extract useful information from this data for making correct management decisions: to get to the bottom of the reasons for the fall in sales in a particular commodity category, to notice new trends, to track changes in consumer preferences, to take into account not only the impact of seasonality, but also trends in consumer activity on certain days of the week and at certain hours during the day. Standard reports of transactional logistics systems are not suitable for such tasks. With each new request, they are forced to start, time and again, from scratch, the harrowing handling of billions of transactions accumulated over many years. And you need to get the results of the analysis and check your assumptions as quickly as possible in order not to lose the logical thread of cause and effect relationships, waiting for many hours of calculations of accounting systems. Here you need another class of solutions - analytical systems.

Built on OLAP technologies, they extract information from special analytical "cubes" - data stores pre-calculated for a certain set of analytical tasks, and present it to the end user. Therefore, they do this almost instantly, in a convenient form, with graphical visualization options for maximum clarity and quick understanding of the essence, allow users to flexibly set sets of parameters and conditions for analysis to look at what is happening from different angles.

Why OLAP technology?

In short, it is this technology that can provide speed and clarity in analysis.

OLTP (On-Line Transaction Processing) systems are designed to record events, such as the arrival, transfer or sale of an item, and for a number of objective restrictions they do not meet the requirements of data analysis.

Therefore, a new class of so-called "decision support systems" based on the principle of online data processing (OLAP - On-Line Analytical Processing) was born. The basis is that the data is stored in a special database, according to its structure, specially designed for information analysis.

This technology provides a number of advantages over the one used in the accounting (transactional) system:

• A multiple increase in the speed of obtaining the data of interest compared to a regular transactional system (for example, it takes several seconds to generate a graph of the dynamics of sales or inventory of a large trading enterprise over a year).
• It is possible in various aspects to consider a large amount of data over a long period of time and do it as quickly as possible.
• Presentation of information in the most convenient form for the user.

One of these systems - DiAna: Digital Analytics Pro - was developed by FIT - France Informatique & Technologie.

DiAna: Digital Analytics Pro is a specialized BI-class analytical software product specifically designed for retailers. Ready for accelerated implementation and compatible with any transactional accounting system, it allows you to quickly process terabytes of data, which is especially important for large chain retailers.

What does DiAna: Digital Analytics Pro do? Who and how can she help?

Using Information Hidden in Cash Checks

Retail sales data from POS terminals and displayed in cash receipts are the most important source for analyzing the key performance indicators of a retail enterprise. They allow you to form an idea of ​ ​ purchasing preferences, track customer reaction to changes in the assortment, which means that you predict demand and prepare appropriate offers for the target group of buyers, consciously approach the positioning of the trading enterprise, effectively manage the assortment and determine the level of inventory.

The DiAna: Digital Analytics Pro decision support system is focused on intelligent processing of such data.

DiAna: Digital Analytics Pro - an analytical decision support system for managing sales and inventory, customer and supplier relationships

The DiAna: Digital Analytics Pro analytical system is addressed to those network retail companies that want to improve logistics and interaction with suppliers, increase customer loyalty.

• is designed to organize the work of category management, marketing, procurement and logistics departments.
• is built on OLAP technology for analyzing sales data from cash checks and inventory data. It allows companies to respond faster and more efficiently to changes in the structure of consumer demand and, based on this, optimize the level and process of replenishment.
• is a means of solving the problems of category management, optimization and assortment management. It helps to determine the profile of the main buyers of the store and their preferences, the assortment they need, sensitivity to price fluctuations, as well as identify weekly and seasonal trends.
• allows you to evaluate and rank suppliers by their contribution to sales by individual product categories, as well as determine their role and weight in the business of the chain retailer as a whole.
• allows you to conduct an in-depth analysis of personal marketing results, assess the effectiveness of the actions carried out and their consequences.
• Conduct separate basic sales and inventory analyses as well as their combinations, as well as in-depth matrix and econometric studies.

I. Basic Sales and Inventory Analyses in DiAna Analytical System: Digital Analytics Pro

Sales Progress Analysis

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These graphs do not reflect the full range of analysis types and their combinations, as well as visualization forms, and are a simple illustration of the graphical representation of solutions to individual types of analytical and research problems.

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With this type of analysis, you can:

• Assess sales trends for a particular item, item group, or department
• Track sales by store
• estimate seasonality of sales, including weekly sales;
• predict the size of the "surge" in sales of certain goods on holidays;
• Understand how demand for goods has changed over time, what is the contribution of a product (or category of goods) or store to the business performance of a company.

The analysis objects can be stores, goods, discount customers, suppliers.

The analysis of sales dynamics can be carried out according to four criteria:

• gross profit;
• revenue;
• quantity of goods sold;
• discount card discount amount.

Hourly Sales Analysis

Using hourly sales analysis, you can:

• assess the dynamics of changes in the purchasing flow;
• Determine the purchase time of personalized customers
• assess the load of cashiers and optimally plan their work schedule;
• determine the best time to display and import the goods;
• more precisely plan the best time for holding promotions;
• Identify Out of Shelf periods.

The analysis objects can be stores, goods, discount customers, suppliers.

Criteria can be:

• gross profit;
• revenue;
• Quantity of goods sold
• the amount of discount cards.

Analysis of sales on multiple checks grouped by purchase amount intervals (Positioning)

With this type of analysis, you can:

• Identify the target group of buyers who generate the bulk of the profit
• Identify the difference between weekend purchases and a worker
• Evaluate the distribution of profit or revenue by check from different purchase amount intervals
• Compare product preferences in different purchase amount intervals.

Stores can act as analysis objects.

The criteria for analyzing sales by purchase amount intervals in the system are:

• gross profit;
• revenue;
• Number of checks or item scans belonging to the selected check group
• Quantity of goods sold
• the amount of discount cards.

Note: You can set the purchase amount interval scale arbitrarily.

Analysis of cash checks by purchase amount intervals provides the most important information:

• What range is in demand by buyers who bring in the main income?
• How much money do they spend visiting the store?

This analysis provides an opportunity to answer a very important question: "Who is the buyer of our chain (or a separate store of the chain in a certain place)"?

It is possible that initially you positioned your store as offering a predominantly inexpensive assortment, but after analyzing by purchase amount intervals, we found that a significant part of customers prefer (or have since begun to prefer) a more expensive product and have the opportunity to make more expensive purchases. This information will allow you to adjust your store positioning based on changes in your customer preferences.

The Positioning analytical unit is an indispensable tool for creating individual customer loyalty systems.

The purpose of the analysis is to find out not only what profit or revenue each group of buyers generates, but also how many checks or scans of an item are included in each of these groups.

The results can be seen in bar or pie charts, which show the shares of gross profit, revenue, or quantity sold depending on the total purchase amount (amount of check).

Inventory Dynamics Analysis

This type of analysis allows you to:

• evaluate the rhythm of procurement;
• Analyze major inventory trends
• Identify Out of Stock
• identify the re-sorting of goods;
• detect overfilling;
• find stale ("forgotten") goods;
• assess the efficiency of the use of working capital;
• Define the inventory/sales ratio for individual items
• identify "failures" in sales associated with the lack of goods in stores.

The analysis objects can be stores, goods, suppliers.

Inventory valuation criteria can be:

• Inventory at average accounting prices, supplier prices, or sales prices
• quantity of goods in stores.

The graph of the dynamics of inventories for individual goods is a characteristic "saw": peak on the day of delivery and gradual decline due to daily sales, then the next peak  , etc.

This type of analysis allows you to evaluate the performance of a category manager or purchasing manager.

II. Types of matrix data analysis in the DiAna analytical system: Digital Analytics Pro

ABC-XYZ sales analysis

This type of analysis allows you to:

• Group vendors or goods by their effectiveness
• identify the best/worst products;
• identify the best/worst suppliers;

This analysis allows you to answer the following questions:

• Which goods sell well (a lot) and/or generate maximum revenue/gross profit?
• Which items carry little weight in gross profit or revenue?

The analysis objects can be stores, goods, discount customers, suppliers.

Criteria can be:

• gross profit;
• revenue;
• Quantity of item sold.

The results of the analysis can be presented as a table or pie chart. In tabular form, to simplify the user's work, color indication (coloring) of lines is used - nine colors by the number of identified segments.

After the analysis for all products, you can change the selection criterion by specifying a specific product category, and conduct a detailed analysis already inside it. Such an analysis will show how you can influence the results of sales in the selected commodity category - for example, exclude poorly selling goods, or hold an action to stimulate their sales  , etc. Seeing such a picture, you can part with outsider goods and make room on the shelf for leading goods.

Modified Dibb-Simkin assay

This type of analysis allows you to:

• classify goods or suppliers into four groups according to the ratio of their sales to gross profit/cost of sales;
• determine the direction of development of the selected segment;
• Identify priority items for assortment, effective suppliers
• assess the effectiveness of the assortment structure, the composition of suppliers, the feasibility of marketing activities;
• Define the main optimization paths.

The analysis objects can be stores, goods, discount customers, suppliers.

Analysis criteria can be:

• gross profit;
• revenue;
• cost.

If the analysis criteria are selected, for example, revenue and gross profit, then as a result we can obtain the following classification of goods with appropriate recommendations for increasing profit:

• The most valuable group for the retailer is A. Goods or suppliers included in this group can serve as performance benchmarks. It is necessary to strive to increase the number of positions in this group, since the increase in sales in this group has the greatest impact on the profit of the enterprise.
• The V1 group should identify ways to increase profitability (a reasonable increase in retail prices, the selection of suppliers with lower selling prices). Due to high sales volumes in this group, even a slight increase in profitability will lead to a tangible increase in the company's profit as a whole.
• The V2 group, which has high profitability, needs to increase sales volumes (promotions, advertising, etc.).
• Least valuable for the enterprise - group C. It is necessary to consider the possibility of replacing individual items from this group, as well as evaluate the effectiveness of excluding the least profitable goods.

Having carried out such segmentation, the company can determine the prospects for development for the near future, direct resources to increase the profitability of the business, develop various strategies for maintaining or restoring the balance of its product portfolio, and optimizing the pool of suppliers.

Modified Boston Matrix Analysis

This type of analysis allows you to:

• Classify goods or suppliers into six groups, depending on how much they hold in the company's total sales, and what is the rate of growth/decline in their sales in the analyzed period compared to the comparison period;
• To develop priority impact strategies, such as pricing, in relation to each of the identified groups;
• evaluate the effectiveness of work on optimizing the assortment and pool of suppliers, based on the dynamics of their movements in the matrix cells;
• achieve a balanced assortment, optimize the pool of suppliers by introducing and supporting promising products through profit from current sales leaders.

The analysis objects can be stores, goods, discount customers, suppliers.

Criteria can be:

• gross profit;
• revenue;
• Quantity of item sold.

In our modification of the Boston Matrix, we segment into six groups: two additional separate categories are allocated goods/suppliers/stores with negative sales dynamics, which is often convenient in analysis. In the traditional Boston matrix, they are usually combined with goods that have low growth rates. If necessary, by pressing the appropriate buttons on the control panel, you can get a classic division into four types: "stars," "cash cows," "wild cats," "dogs." There is a priority strategy for each of these groups.

• Goods with a low growth rate and a large share in sales - "cash cows," in accordance with the name, bring in a lot of money. These are well-known, traditional, always in demand products. Therefore, they become a source of funds for the development of the company.

• "Stars" have a high growth rate and make a lot of profits. As a rule, these are successful products that have proven themselves to be new with good advertising. "Stars" generate significant returns that can be invested in maintaining their sales. At the stage of maturity, after reaching the maximum coverage of consumers, these groups of goods usually turn into "dairy cows."

• "Dogs" have low sales and low or negative growth rates. This group of goods must be carefully studied. As a rule, the best solution will be to consider the feasibility of removing them from the assortment, based on their profitability. At the same time, as an exception, it is worth leaving some related goods that are needed to maintain the assortment and stimulate the sale of other, more significant goods.

• And finally , "wildcats" have high growth rates, but sales are still small. This is the most uncertain position, as a rule, these are goods entering the market, new items. If they are rated as promising, then it makes sense to invest money in their promotion, to transfer them to the category of "stars." If the novelty does not take root and quickly gets tired of consumers, then sales growth will gradually slow down, and "wild cats" will move to the category of "dogs."

III. Econometric Data Analysis in DiAna Analytical System: Digital Analytics Pro

Econometric analysis allows you to evaluate changes by week or by month/day for one or more stores or departments/groups/subgroups/goods of the following characteristics:

• Average return on inventory
• inventory turnover;
• the value of inventory maintenance;
• income from the linear meter of the retail area of ​ ​ the shelves;
• sales per square meter of retail space.

Rationale for DiAna: Digital Analytics Pro

• Specialization. The advantage of the DiAna: Digital Analytics Pro system is that it is a vertical solution for the retail market: it is not only the tool itself, but also a ready-made set of pre-configured "cubes" for a number of analytical tasks most in demand by retailers.
• Versatility in docking. DiAna: Digital Analytics Pro works not only with FIT's GESTORI Pro system, but can also be integrated with any (!) other retail merchandise management system already used in your retail network.
• Minimum implementation time. If the retailer already has the GESTORI Pro system implemented, it takes no more than 2 working days to install and launch DiAna: Digital Analytics Pro. This does not require the presence of a FIT specialist (or its partner) at the customer's site: a remote consultation is enough. Moreover, the qualifications of those employees who will install the system may be quite low. If you have to integrate with any other cash and accounting system by FIT specialists, then up to 20 working days are allowed to agree on formats and then upload data from these packages to the DiAna: Digital Analytics Pro system. Moreover, the integration process is so simple that the retailer can use its IT department to independently upload data in a pre-agreed format or use the services of the supplier of the accounting and cash systems it uses.
• Multi-criteria and flexibility. A big advantage of the DiAna: Digital Analytics Pro system is the ability to quickly move from one analysis criterion to another. Thus, in particular, a set of elements for analysis can be formed arbitrarily. As a result, it becomes possible to compare, for example, sales by beer categories and by their brands, even if the classifier implies division by only one of these features. In addition, the original item master data can have any number of classifier nesting levels.
• Combine different types of analysis. DiAna: Digital Analytics Pro allows you to simultaneously analyze, for example, inventory and sales indicators for a specific product on one graph in order to quickly identify possible underorder/reorder or supply interruptions.

In addition, you can compare the lists of items generated in different types of analysis using different criteria, find their intersection, or combine them into one list for subsequent analysis or copying to any office program to create a report. For example, the coincidence of goods with worse positions in both ABC-XYZ analysis and the modified Boston matrix signals that these are clear candidates for withdrawal from the assortment, increases the validity of such a decision.

When the product "limps" on several types of analysis, it is worth thinking, is it needed at all? "]]

• Interface. In-depth training of employees is not required: our clients often use the system directly at meetings of category managers, boards of directors, during the discussion demonstrating certain calculations, illustrating their theses. In addition, the methods, for example, ABC-XYZ analysis, of the Boston matrix are generally accepted, known to financiers and category managers.

2004-2006: System Development

In 2004-2006 The development of a specialized BI system for retail DiAna: Digital Analytics Pro was carried out.