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Qusai Sarraf and Gerard Ellis of IVIS describe rules-based B2C and B2B systems in the biggest on-linegrocery store in the world, with the business rules owned by business people -- John Hall

Business Rules in Retail: The Tesco.com StoryQusai Sarraf and Gerard Ellis

IVIS Group Limited, CP House, 97-107 Uxbridge Road, Ealing, London, W5 5TL info@ivisgroup.com.

AbstractUK-based Tesco.com is the Internet arm of Tesco Plc, the third-largest retailer in the world. Tesco.com isthe largest on-line grocery store in the world, with annual turnover exceeding $2b; it also retails electricalproducts and other goods from its parent. It has adopted the Sonetto platform from IVIS Group to developsystems driven by business rules in several different contexts, including on-line purchasing of wine andelectrical goods, interaction with affiliate companies and shopping comparison services, and provision ofhealth and dietary information to customers. The business rules in all of these applications are developedand maintained by business users.

IntroductionBricks and mortar retailers are investing in multi-channel initiatives including online stores and cataloguesto broaden their market reach and gain competitive advantage. At the same time, diversification andcollaboration initiatives are growing. Diversification initiatives include increasing the range of products andservices offered and entering new markets and geographies. Collaboration and partnership strategies createalliances in the sourcing, fulfilment and sales functions that broaden market reach.

All of these strategies entail a massive increase in the amount of information a retailer has to manage. Weassert that the only effective solution in dealing with the challenges of retail information management is toturn to the most valuable asset in any organisation, knowledge. However, organisational knowledge is vastand irregular and there are many examples of failed attempts to tame the process of acquiring andharnessing knowledge. Turning to Enterprise Business Rules is the logical approach; however, the questionis how to commercialise the process of creating business rules and provide this mechanism to those whoneeded it - the business users.

Tesco.com is the largest online grocer in the world with annual turnover exceeding $2b. It is the online arm

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of Tesco Plc., the third largest retailer in the world with annual revenue exceeding $70bn.This article tellshow Tesco.com partnered with IVIS Group, a UK based multi-channel e-business expert, to meet thechallenges of enterprise business rules head-on - and win.

ChallengesTraditionally, retailers invested heavily in optimising their supply chains and processes. EnterpriseResource Planning (ERP) initiatives that have taken years to implement and cost millions of dollars aregeared towards streamlining processes which have been maturing for many years - in buying, transporting,storing, merchandising and subsequently selling products. These traditional systems have led to ‘stove pipe’information architectures that are hindering rather than helping the implementation of new strategies.

Retailers are facing the following challenges in striving to implement multi-channel informationmanagement capabilities:

Multiple sources of increasingly dynamic product informationPoor quality of information from an increasing number of 3rd party data providersMultiple internal and external stakeholders, especially customers, with different informational needsdepending on the channelAbsence of standard definitions of data and processesIncreasing customer expectations for richer product information through multiple channels

Dealing with the challengesRetailers have taken a number of steps in dealing with the above challenges. These measures range fromoutsourcing manual data management to building technically driven in-house applications and purchasingdedicated IT solutions.

IT investments have typically included catalogue management systems, search tools, data extraction andcleaning tools, category and catalogue management systems and Product Information Management (PIM)solutions. However, these solutions typically suffer from over-reliance on IT, fixed product definitions orschemas and the need for manual, time consuming and expensive cleaning and enrichment of data.

Sonetto, a unique solution to a common problemSonetto is a rule-based Multi-Channel Product Information Management solution targeted at business users.At the heart of Sonetto there is an enterprise rule engine which provides services to store and retrievesemantic information. Business users interact with the core rule engine through three applications:

Enrichment engine – Defining new products, editing and extending existing products and attributesCategorisation engine – Slicing and dicing product information in different categories, providingdifferent views to multiple stakeholdersDiscovery, or Search engine – Searching, validating and discovering information, associated patternsand relationships by business users and end-customers

Figure 1 shows the Sonetto component architecture:

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Figure 1: Sonetto component architecture

Overview of Sonetto at Tesco.comSonetto is being used by Tesco.com in three different areas that demonstrate the value of business rules asthe basis for solving very different information management challenges:

Tesco.com uses Sonetto to manage product information for its Electrical and Wine categories, allowingbusiness users to take control of the quality of product information regardless of the quality and lack ofstandards in the source data. This allows business users to provide online customers with content-rich,customer-centric information and demonstrates a Business-to-Customer (B2C) application for businessrules.

Affiliate networks and Shopping Comparison services (like Kelkoo) generate a large proportion ofTesco.com’s electrical and entertainment revenue. However, there were many challenges in managing thischannel including the need to communicate with a myriad of partners who require product data accordingto different definitions, formats and categorisation structures. Tesco.com business users use business rulesto fulfil the Business-to-Business (B2B) function of managing the product data feeds to these partners.

Finally, to capitalise on the increasing demand by consumers for health and dieting information andservices, Sonetto was implemented for a third time in Tesco.com to enable business users to create dynamicrule-based linkages between recipes, ingredients and products. This demonstrates a Business-to-Employee(B2E) application.

Principles of the Sonetto Knowledge Management Approach

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Sonetto has been designed using the following principles:

Business rules are written by business users, not IT nor restricted to specialist rule authorsIndividual tasks or domains require specific sser interfaces for rule building for general usersBusiness users build rules in-situ through normal editing or interaction with a system and its contextRule building is activity centric in a rich set of business workflowsA maintenance approach is the foundation as it is assumed rules are incomplete and will evolve overtimeUsers must be given confidence in the system by showing impact of rule change in real time(testing), through evaluation over a repository. Users need to see actual cases affected, andsummaries or auto categorisations of groups of affected casesRule building scales at the human level through users not focusing on rules, but on editing documentsin normal interactions, and justifying changeRules and their interdependencies are managed by contexts using Ripple Down Rules, so that changeis localised to the current context, resulting in stable rule buildingAllow users to say or interact as needed through a bottom up approach to schema management whichmeans no IT involvementUse powerful knowledge representation to capture contexts, rules, categories, schemas. We use theConceptual Graph formalism as Sonetto’s foundation.

Individual Tasks or Domains Require Specific User Interfaces forRule BuildingSince general business users hold much of an enterprise’s knowledge, a goal is to capture that knowledgewhilst minimising any change or training required to capture that knowledge. This means that Sonetto isideally invisibly integrated into existing applications or used to develop specific interfaces for the tasksundertaken by the user. Whilst it is possible to write declarative rules in some logic, or formal controlledlanguage such as Controlled English [4] , or use systems such as RuleSpeak [1], many users would prefer ifthey were not presented with such an open-ended opportunity to build rules. Rather a simpler task ofjustification of the change made in context is more achievable for many users.

A major part of rule building is identifying the conclusion or action to be taken in the context of a giventask with some data. Capturing this interaction or change by the user is required. Typically, this involvesadding, removing or modifying some information. The information removed or modified may or may nothave been previously generated by existing rules captured by Sonetto.

Sonetto provides specific user interfaces for product management, product standardisation, promotionmanagement, and has been the basis for the recipes solution. Sonetto then captures the change, the case ordata being changed and then allows the justification of this change in user friendly interfaces. Sonetto canbe integrated into existing applications or extend those applications as well.

Kinds of Rules AcquiredThe following examples illustrate the kind of rules that have been captured using Sonetto at Tesco.com inthe Product Information Management and Affiliate Management applications, though they are forexplanation purposes only and are not actual Tesco.com rules. Rules 4 b) and 7 illustrate customer targetedrules which are possible as suggested as key for personalisation [8] .

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1. If a television product description contains “28-inch screen”, add a screen_size attribute_inches witha value of 28.

2. The click through rate for the Shopping comparison service Kelkoo is the negotiated value of 50cents for camera accessories.

3. Add the attribute ‘type’ with value ‘Playstation’ to Kelkoo affiliate product information if categoryhierarchy level 2 is ‘Gaming’ and gaming type is ‘PS1’.

4. a) If a recipe ingredient contains butter, suggest “Gold Butter” as an ingredient to add to the basket.b) If the customer prefers organic dairy products, suggest “Organic Butter” as an ingredient to add tothe basket.

5. If a customer buys two packets of biscuits, the customer gets one free.6. If the basket value is over £100, delivery is free.7. If the customer is a family with children, suggest buy one family sized pizza and get one free.

The first rule illustrates the type of rule that would be built during a product standardisation activity. In thisactivity unstructured product data would be converted into a standard structured data model.

The second and third rules are examples of rules that would be constructed in mapping product data to ashopping comparison partner such as Kelkoo.

The fourth a) and b) is the kind of rule that would be captured in a recipe management system and the fifth,sixth and seventh are simple examples of rules that would be captured when managing promotions.

Ripple Down Rules: A Maintenance approach to Rule AcquisitionSonetto is based on the Ripple Down Rules (RDR) framework for rule acquisition [5] [6] . Users makeconclusions about a case. In Sonetto, a ‘conclusion’ corresponds to some editing action such as changing,adding, or removing an attribute value or relationship. Cases in Sonetto can be products, customers,recipes, etc. When a user changes an attribute value of a case, they are asked to justify the change (addconditions for the rule). This rule is added in the context of the rule that gave the modified attribute value.

The following shows the steps to add the rule “Products over £250 have a delivery cost of £0.00.” A userwould already be in a category of products such as Electrical > Home and be looking at a product such as“Hotpoint Washer Dryer”

Add attribute delivery_cost with a value of £0.00Justify the edit because price > £250 by selecting this condition from the Hotpoint Washer Dryer.

The RDR rule tree is simply

If price > £250 ThenDelivery_cost is £0.00

An exception to this rule may be “Products which weigh more than 100 KG have a £10 delivery charge.”The user would create this rule when they had been reviewing a product such as “Double Fridge/Freezer”which has the inferred delivery_cost from the previous rule. They would then:

1. Modify the delivery_cost from £0.00 to a value of £10.002. Justify the change because weight > 100 KG.

The modified delivery_cost of £0.00 already had a condition (context) of price > £250, so the user is only

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adding a portion of the rule. This context based editing means any change is constrained to minimiseunintended consequences.

The RDR rule tree is then modified to

If price > £250 ThenIf weight > 100KG Then

Delivery_cost is £10.00Else

Delivery_cost is £0.00

Note that a major part of the task of rule building is editing or interacting with the current task and context.The justification part can be done asynchronously by the same or different users or a machine learningsystem.

The user could then be reviewing these rules coming up to a holiday period, where they want to put on adelivery charge of £5.00 from the normal £0.00.

The user could select some product say the “Hotpoint Washer Dryer” used previously or other withdelivery_charge £0.00. They then do the following tasks.

1. Modify the delivery_cost from £0.00 to £5.002. Justify the change because holiday_period

The RDR rule tree is then modified to:

If price > £250 ThenIf weight > 100KG Then

Delivery_cost is £10.00Else

If holiday period ThenDelivery_cost is £5.00

ElseDelivery_cost is £0.00

The complexity of the steps of editing and justification are fixed, whilst the decision trees that aremaintained by RDR are growing in complexity. Nested if-then-elses with cyclomatic complexity greaterthan 10 (number of paths through the code) are not maintainable [11] . Experience at Tesco.com has shownthat the rule bases built performing their tasks are very complex when seen in a nested form or in a purelydeclarative form. RDR scales because users do not need to see the rule trees. They are verbalised on thecases as justifications for the conclusions already added by the rule base. Consequently users don’t havesearch or browse for rules to change, they are referenced on documents/cases affected, they only have tomodify any existing conclusion and justify the change.

User Confidence in Impact of Rule ChangesUsers need to understand the impact of adding a rule to the system.

RDR is rule building by example. The example is the case that was edited before going into the rule builderfor justification, and is called the current case, or cornerstone case. When building the conditions for the

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justification, users are presented with the current case and the cornerstones cases of the other rules that areaffected by the rule under construction. The set of cornerstone cases affected by the rule is called theconflict set.

Initially a justification (condition set) of a rule is empty, so it affects all cornerstone cases. As the user picksconditions from the current case, the condition becomes more specific. This shrinks the conflict set, as lesscornerstone cases will match the justification.

To exclude a cornerstone case from being affected, the user picks the excluding features from that case,which are turned into a negative conditions. This again will shrink the conflict set.

In the previous example, modifying the delivery cost from £0.00 to £10.00, was done in the context ofproducts with prices > £250. The user could have chosen from the current case that weight > 100KG. Afterthey have added the condition they would then be left with conflict cases where prices > £250 and weight >100KG. The user refines the conditions until the conflict set consists of those cases the user intends to havethe rule applied to.

RDR is typically implemented in two separate activities: validation and rule building. In the validationactivity, cases have been enriched by the rule engine and are reviewed or possibly edited (corrected) by theuser. In the rule building phase, a user reviews cases which have been modified by the user or other users.A rule is added to automate the editing change. RDR systems are highly accurate, usually achieving 98%correctness or higher [6]. Hence the number of cases reviewed in the rule building activity is 2% of thoseseen in the validation activity as shown below in the transaction model.

Figure 2: Rule and Case Maintenance

This separation of validation from rule building is particularly suited to the pathology applications whereRDR has been used. This is because pathologists typically cannot modify pathology reports which havealready been issued. Hence, only examples used to build rules are recorded.

In Sonetto, as well as maintaining rules, users are also maintaining all the cases in a repository (see therepository model in figure 2). Each rule can potentially edit a large proportion of a vast repository of cases.Consequently, when justifying a change in the rule builder all cases are considered not just cornerstonecases.

In RDR, normally several cornerstone cases need to be reviewed, but in our case we have potentially orders

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of magnitude more cases to review. The number of conditions added for each rule is still the small numberexperienced in other RDR systems. Sonetto users can review the cases affected through an automatichierarchical view of the cases constructed over relevant attributes. They can navigate through these cases tovalidate there are no further exceptions to the rule.

Each conclusion is a category of products which can also be explored, so reviewing a set of rules whichgive the same conclusion.

RDR supports continuous testing similar to that proposed by Berlandier [7].

Efficiency of Rule MaintenanceWe have confirmed what has been shown in other domains that RDR systems allow general users to add arule in a few minutes independent of the number of rules in the system [6] [4] . What normally took severaldays of IT time, and also that of commercial people instructing IT, takes several hours of the commercialperson’s time per month to maintain product data. The quality is also much higher.

Schema Management: Bottom-up and Top-DownSonetto supports both top-down and bottom-up approaches to data schema management. Whilst there iscentral control over the semantic schema that allows a design first approach, Sonetto supports usersextending schemas in a controlled fashion through rule-based editing of product data. This eliminates theneed for costly IT staff and software delivery cycles to support this open-ended growth and maintenance ofthe data. Users can alternatively, use an administrator to control changes to the schema and control usersediting of data.

A Solid Logical foundationSonetto uses the Conceptual Graph (CG) logic formalism [2] as its basis for representing documents, rules,categories and schemas. CGs express meaning that is logically concise and human readable. There are anumber of forms of CGs including the computer readable CGIF form, and a graphical notation. CGIF ispart of the forthcoming ISO Common Logic Standard [3] . For example, the following graphicalConceptual Graph represents that a product has a price of £251.00 and a delivery_cost of £0.00.

A CG consists of two kinds of nodes: concepts and relationships. The following diagram shows the samestatement in the graphical form.

Figure 3: Conceptual Graph (CG)

In this example there are 3 concepts: a product, and two currency amounts. There are two relationships:price relating product to a currency and delivery cost relating the same product to a currency. The followingis the computer processable CGIF form

[Product: *p] (price ?p [Currency: "£251.00"]) (delivery_cost ?p [Currency: "£0.00"])

A Unique combination of Scalable Rule Engines and Semantic

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Search EnginesSonetto requires highly scalable semantic search engines and rule engines because of the volumes of datainvolved. The semantic search engine is required to find and navigate through the affected documents whenrule building and categorisation of documents according to conclusion or other attributes within documents.

Sonetto builds on the knowledge processing work done in [9] and [10] which focused on classification ofConceptual Graph contexts as a basis for organising knowledge. This is a central operation in a semanticsearch engine and a rule engine. Sonetto’s engines are a fourth generation of these techniques.

Semantic Rule Search Engine Example CaseConsider application of rules to the following situation:

“A recipe with ingredients milk, butter and sugar which is presented to a customer who prefers organicfood.”

Figure 4: CG of Recipe

Now given a rule

“if a recipe with a butter ingredient is presented to a customer who prefers organic food, then suggest“organic butter” (is added to the basket)”.

Figure 5: CG of Recipe with rule

After the rule is applied, we have

“A recipe with ingredients milk, butter and sugar which is presented to a customer who prefers organicfood, where ‘Organic Butter’ is suggested to add to the basket for the butter ingredient”.

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Figure 6: CG of Recipe after application of rule

Here the key operation is projection [2] where the graph representing “a recipe presented to a customerwho prefers organic food” projects into (is a generalisation of) the graph for “A recipe with ingredientsmilk, butter and sugar which is presented to a customer who prefers organic food.”

A search for generalisations is the first phase of the two phase problem of classification discussed in [9] and[ [10] . The highly efficient Sonetto Rule Engine uses generalisation search to find rules which apply to amore specific situation/case.

Semantic Search EngineSemantic Search [13] is a kind of projection or matching of a query into the semantic network which is thesemantic web.

The following shows some possible queries over the semantic web: “wine”, “Australian wine”, “AustralianMerlot”, “Nick Bruer Merlot”, “Temple Bruer Reserve Merlot” which are refinements of each other,respectively.

Figure 7: Refinement of Semantic Web queries

Each of these queries is a generalisation of the Temple Bruer Merlot Reserve below. This search forspecialisations is the second phase of the classification algorithms in [9] .

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Figure 8: Specialised Semantic Web query

By using Sonetto’s rule acquisition system, Sonetto can take unstructured data and highly structure it, tobuild highly enriched structured data. This enables ever richer semantic search and exploration.

Commercialisation of the Semantic Web 2.0Folksonomies [12] are an example of categorisation and rule building that Sonetto has sympathy with.Rather than focus on bottom-up development of Taxonomies, we are focused on approaches that allow folkto categorise and tag in an open way as they require, whilst providing support to build a folk or communityview.

ConclusionAt the heart of the challenges facing retailers today is the need for the business to take control of theprocess of information management. Traditionally, this process is left in the hands of the IT department.However, to compete, organisations need to be agile so they can grasp opportunities in an increasingchallenging, dynamic and complex market.

The key to addressing these challenges is to harness business knowledge to manage the information. Hencethe need for an agile and business-focused system that takes a holistic approach; serving the needs of allstakeholders including customers, partners and employees.

This paper has outlined how Tesco.com uses business rules through Sonetto to achieve its targets and togrow the most successful online grocer in the world.

References[1] Ronald G. Ross, "The RuleSpeak® Business Rule Notation," Business Rules Journal, Vol. 7, No. 4(Apr. 2006), URL: www.BRCommunity.com/a2006/b282.html

[2] John F. Sowa, “Conceptual Structures: Information Processing in Mind and Machine,” Addison-Wesley, Reading, MA (1984).

[3] Information technology — Common Logic (CL) – A framework for a family of logic-based languages,ISO/IEC JTC 1/SC 32 N 1377, 2005-12-10,ISO/FCD 24707,ISO/JTC 1/SC 32/WG2,ANSI working draft.URL: http://cl.tamu.edu/docs/cl/32N1377T-FCD24707.doc

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[4] John F. Sowa, “Controlled English” (1999),URL: www.jfsowa.com/logic/ace.htm

[5] Paul, J. Compton and Robert Jansen. “A philosophical basis for knowledge acquisition.” KnowledgeAcquisition 2: 241-257 (1990). (This paper first appeared in EKAW 89)URL: www.cse.unsw.edu.au/~compton/publications/1989_EKAW.pdf

[6] Paul Compton, Lindsay Peters, Glen Edwards, and Tim G. Lavers, “Experience with Ripple-DownRules,” Knowledge-Based System Journal. (2006 in press) (This paper first appeared in AI2005, the BritishSGAI conference) URL: www.cse.unsw.edu.au/~compton/publications/2005_SGAI.pdf

[7] Pierre Berlandier, "Changing the Rules of Testing ~ Testing Strategies for the Production Maintenanceof Rapidly Evolving Business Rules Systems," Business Rules Journal, Vol. 7, No. 2 (Feb. 2006),URL: www.BRCommunity.com/a2006/b270.html

[8] Rolando Hernandez , “Business Rules are the Key to CRM and One-to-One Personalization”, BusinessRules Journal, URL: www.brcommunity.com/b064.php

[9] Gerard Ellis, Robert Levinson, Peter J. Robinson: “Managing complex objects in Peirce.” InternationalJournal of Human-Computer Studies 41(1-2): 109-148 (1994)

[10] Gerard Ellis: “Compiling Conceptual Graphs.” IEEE Transactions on Knowledge and DataEngineering 7(1): 68-81 (1995)

[11] “Cyclomatic Complexity” URL: en.wikipedia.org/wiki/Cyclomatic_complexity

[12] “Folksonomies” URL: en.wikipedia.org/wiki/Folksonomy

[13] R. Guha, Rob McCool and Eric Miller, “Semantic Search”, The Twelfth International World WideWeb Conference (2003). URL: www2003.org/cdrom/papers/refereed/p779/ess.html

about ... Qusai Sarraf

Qusai Sarraf, CEO of IVIS Group, is an entrepreneurpassionate about identifying and growing e-businessopportunities. His background, spanning 20 years inIT, combines e-business strategic consulting, early e-commerce architecture development and designingbusiness-focused solutions. Qusai was one of theseven members of the team that built Tesco.com in1996 and he continues his role in steering thestrategic direction of a number of high profileprojects.

Qusai started IVIS Group in1994 and organically grew the businessbased on a professional services model. In 2000 he started a productdevelopment venture to further expand the business which hasculminated in developing Sonetto, a world-leading business rulesbased PIM solution. Today, IVIS Group is a 40 person strongcompany with £3m turnover. Qusai has a degree in Electronic Engineering from Brighton Universityand moved early in his career to the software industry where heworked for various software houses before setting up IVIS Group. Heis an international public speaker, author and trainer specialising inusing web standards and technologies to resolve business problems.He is the author of the best selling Learning Tree Internationalcourse "Building XML Applications" which is taught around the worldand has been translated into a number of foreign languages includingFrench and Japanese. He may be contacted at info@ivisgroup.com, or on +44 (0) 20 8579

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He may be contacted at info@ivisgroup.com, or on +44 (0) 20 85793335 .

about ... Gerard Ellis

Gerard Ellis, Chief Scientistof IVIS Group, has 16years of academic and commercial experience.Gerard’s PhD thesis was on managing knowledgeencoded in Conceptual Structures, and hisbackground is in building logic program compilers,knowledge acquisition and processing, and e-commerce solutions. Gerard has numerous conference and journalpublications. He also chaired several internationalconferences on Conceptual Structures.

At IVIS Group, Gerard continues to lead research into scaleableknowledge processing systems for business workflows. He may be contacted at info@ivisgroup.com, or on +44 (0) 20 85793335 .

standard citation for this article:Qusai Sarraf and Gerard Ellis, "Business Rules in Retail: The Tesco.com Story" Business Rules Journal,Vol. 7, No. 6 (Jun. 2006), URL: http://www.BRCommunity.com/a2006/n014.html

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