the modern retailer’s guide to product … › uploads › 2019 › 08 ›...

Dynamics 365 for Retail

Microsoft Dynamics 365

The Modern Retailer’s Guide to

Product recommendations

Dynamics 365 for Retail eBook series


The Modern Retailer’s Guide to Product Recommendations | Introduction

From ‘Most popular items’ to ‘Recently viewed’ to ‘Customers who purchased this item also bought...’, there are many different ways that retailers try to signal to customers, “Hey! You might like this product!” Yet not all product recommendations are the same, and their methods are seldom understood. In this edition of the Modern Retailer’s Guide, we’re demystifying Product Recommendations so you can better understand the mechanics of how they work and how you can leverage them for your business.

About the Modern Retailer’s GuideWe’ve developed the Modern Retailer’s Guide to help retailers understand emerging trends, technologies and concepts. Our goal is to balance simplicity, breadth, depth and technical nuance to explain complex topics in a way that is easy to understand while still being thorough and useful for modern retailers.

Introduction


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ChaptersWe’ve broken all of the information down into five easy to understand sections.

01

What are product recommendations?We answer this complex question at the most basic level.

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Benefits and challenges

We discuss the benefits that product recommendations can provide, as well as current challenges.

03

How it works

From content-based filtering to predictive purchase modelling, we’ll explain the six most common methods driving product recommendations.

02

Key concepts

Data classificationsAnalytics models

05

Best practices

We’ll look at some product recommendation best practices, as well as retail relevant use cases.

The Modern Retailer’s Guide to Product Recommendations | Introduction

The Modern Retailer’s Guide to Blockchain | Introduction

Dynamics 365 for RetailDynamics 365 for Retail

The Modern Retailer’s Guide to Product Recommendations | What are product recommendations?

Menu

Chapters

04 Benefits and challenges

03 How it works

02 Key concepts

05 Best practices

01

What are product recommendations?We answer this complex question at the most basic level.

01 What are product recommendations?



At their most basic level, product recommendations, as the name implies, are simply products that a retailer recommends to a customer.

Product recommendations can be surfaced in many different ways – by a knowledgeable store employee, in an email or on a product page of an e-commerce website – and they may include a variety of different messaging to get the customer’s attention. While we will touch upon these topics (i.e. location and messaging) in the course of our review, our primary exploration is a more technical look at the mechanics of product recommendations: the data and logic behind the recommendations.

What are product recommendations?

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So through that lens, we can revisit the question: What is a product recommendation?

A product recommendation includes three parts: the inputs (the data that will inform the recommendation), the algorithm (or method: the logic that calculates an output based on the inputs), and the output (the actual recommended products).

With that knowledge, we can then define a product recommendation as the data output of an algorithm that is run using some (consumer and product) data input. For example, if a customer views three blue dresses on a website, the recommendation engine (algorithm) may take this input (category: dresses; colour: blue) and output similar blue dresses.

The algorithm The outputThe inputs

A customer views three blue dresses on a website.

The data that informs the recommendation.

The customer sees the recommended product(s) based on the metadata of the blue dress and the patterns of customer browsing activity.

The actual recommended product(s).

The recommendation engine identifies common traits between the dresses and finds other items with the same common traits.

The logic that is used to calculate the output based on the inputs.

Three parts of a product recommendation Example: Content-based filtering method

A product recommendation includes three parts: the inputs, the algorithm, and the output.

Category: Dresses Subcategory: Sun dress Attribute: Floral pattern Colour: Blue

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03 How it works

05 Use cases

02 Key concepts

The Modern Retailer’s Guide to Product Recommendations | Key concepts

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Chapters02

Key conceptsData classificationsAnalytics models

01 What are product recommendations?


Key concepts


To help you better understand how product recommendations work, we’ll start by defining a few key concepts.

Data classificationsCategoricalOrdinalIntervalRatio

Analytics modelsSubjective predictionsExtrapolation modelsBayesian modelling

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Data classifications

There are four main ways in which data is classified:


Categorical

Data grouped by one or more characteristics. This is sometimes referred to as clustering or segmentation. Examples include grouping products based on similar attributes or grouping users based on behaviours or demographics.

Audience segmentationM 18-24M 35-54M 55+F 18-34F 35-54F 55+

Ordinal

Data ranked or ordered to show relational preference. Examples include ranking products based on units sold or by customer rating.

Products by Annual RevenueProduct DProduct KProduct BProduct ZProduct RProduct Y

Ratio

Data expressed as a ratio on a continuous scale. This includes probability analysis and scoring (which can, in turn, be used in ordinal rankings). In retail, ratios are used all the time, such as inventory turnover rate, sell-through rate and the average cost of goods sold.

Lift between Q1 and Q2 sales15% increase

Market shareRetailer holds 7.3% market share

Inventory turnover rate2.92

Interval

Data arranged along a scale where each value is equally distant from others. Examples include arranging products based on size or ordering employees based on seniority (where the ‘degree of seniority’ is irrelevant and all that matters is whether someone is above or below another employee in rank).

Customer satisfaction1 – Satisfied2 – Slightly satisfied3 – Neutral4 – Slightly dissatisfied5 – Dissatisfied

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Analytics models

Subjective modelsSubjective models are based on experience, personal judgement and instinct. Subjective input adds a logical, human element to a purely data-driven approach.

On the one hand, a recommendation that a store employee makes based on their personal judgement is a type of subjective prediction (predicting that the customer will like the recommendation). But in this case, subjective predictions also include human-aided categorisation, such as adding ‘tags’ to products based on various product attributes, which recommendation engines may rely on to formulate their outputs.

Extrapolation modelsExtrapolation models predict outcomes based on historical data. These models identify and extend past trends into the future. They may also use past performance to predict future probabilities.

Many retailers are familiar with using these types of models in performing sales forecasting for inventory management; in its simplest form, it says, “Based on what we’ve sold the last three years, this is how much we expect to sell next year”. Prediction engines often leverage historical data, such as previous purchase patterns of similar customers, to formulate recommendations.

Bayesian modellingBayesian modelling not only weighs each independent variable in calculating an output, but also adjusts how it weighs those variables based on the dynamic interactions between them.

Bayesian modelling is generally considered a more advanced type of modelling that can be used in making product recommendations, forecasting inventory demands and optimising logistics processes.


Subjective modelsSubjective models are based on experience, personal judgement and instinct.

Extrapolation modelsExtrapolation models predict outcomes based on historical data.

Bayesian modellingBayesian modelling weighs each independent variable in calculating an output, while simultaneously adjusting how it weighs those variables based on the dynamic interactions between them.

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The Modern Retailer’s Guide to Product Recommendations | How it works

Menu

Chapters


02 Key concepts

05 Best practices

03 How it works

03

How it worksFrom content-based filtering to predictive purchase modelling, we’ll explain the six most common methods driving product recommendations.

What are product recommendations?01


How it works


Subjective models Extrapolation models Bayesian modelling

In addition to these techniques, recommendation engines may use a hybrid approach, leveraging multiple techniques to drive recommendations.

Content-based filteringContent-based filtering makes product recommendations based on similarities in content and context, such as keywords and product attributes.

Proximity matchingProximity matching is a technique that makes product recommendations based on products with complementary features (within close proximity), such as product accessories.

Pattern recognitionPattern recognition leverages historical data patterns to categorise products and make predictions about which products a customer may be interested in based on those categorisations.

Collaborative-filteringCollaborative-filtering – sometimes referred to as ‘look-alike modelling’ – segments users based on interests and behaviours, and then surfaces recommendations to one user based on the patterns of the group.

Predictive audience modellingPredictive audience modelling is similar to collaborative-filtering. Where collaborative-filtering clusters users, however, predictive audience models ordinally rank products based on a predictive score of how likely a given audience is to purchase them.

Predictive purchase modellingPredictive purchase modelling uses advanced modelling techniques to analyse specific user data (demographics, etc.), user behaviour (site usage, third-party behavioural data, etc.), store data (website data, in-store purchase data, etc.) and other relevant variables, as well as the interactions between these variables, to assign prediction scores as to how likely a specific user is to purchase a product. It then surfaces recommendations for products which it determines the customer is most likely to buy.

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Content-based filtering

Content-based filtering makes product recommendations based on similarities in content and context, such as keywords and product attributes.

Step 1: Define keywordsKeywords or tags are defined based on product attributes – which may include category, size, capabilities, features, shape, colour, fit, cost, genre, etc. – or page content.

Step 2: Customer views a product onlineA customer views a product or a page online. The keywords from the product/page are captured.

Step 3: Product keyword comparisonThe recommendation engine compares the keywords against keywords of all other products/pages and identifies the products with the greatest overlap (as defined by the recommendation engine).

Step 4: Product recommendedThe recommendation engine surfaces the most similar products based on the keyword comparison and displays them on the customer’s page.

Customer shopping for shoesKeywordsMen’sTrainersRunningDistance

Data inputs

Filtering against product keywordsRecommendation

EngineKeywordsMen’sTrainersCross-trainingDistance

KeywordsMen’sTrainersRunningSprint

KeywordsMen’sTrainersRunningDistance

Customer browsing the webKeywordsSnowSkiingBackcountryLightweight

Data inputs

Filtering against page keywordsRecommendation

Engine

Ditatum snow skiing essenite backcountry ame harchictiis vsdfaol-or lightweight?

KeywordsSnowSnowboardBackcountryMid-weight

KeywordsSnowSkiingBackcountryLightweight

KeywordsSnowSkiingRecreationalLightweight

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Proximity matching

Proximity matching is a technique that makes product recommendations based on products with complementary features (within close proximity), such as product accessories.

Step 1: Define product relationshipsProducts are assigned keywords and categories, and relationships between these keywords and/or categories are defined. For example, laptops and laptop accessories might be two categories with a defined relationship.

Step 2: Customer views a product onlineA customer views a product or a page online. The keywords from the product/page are captured.

Step 3: Product relationship analysisThe recommendation engine evaluates known relationships based on the product/page category and identifies products in the companion category.

Step 4: Product recommendedThe recommendation engine surfaces relevant products based on the relationship analysis and displays them on the customer’s page.

The recommendation engine may use a hybrid approach, first filtering associated products based on defined relationships, then ranking those products using collaborative filtering (or another technique).

Define relationshipsRecommending product accessories

Laptops Accessories

Mouse

Monitor

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Pattern recognition

Pattern recognition leverages historical data patterns to categorise products and make predictions about which products a customer may be interested in based on those categorisations.

Step 1: Customer engages with site, makes purchases online or in-storeA customer engages with the site – visiting multiple pages and interacting with content – and perhaps makes purchases online and/or in-store.

Step 2: Identify patterns based on past behaviourThe recommendation looks for patterns based on the user data – including demographic and geographic data – and the user behaviour – including pages visited, time of day, time on site, referral site (the external site where the user clicked a link and was directed to the e-commerce site), etc.

Step 3: Forecast future behaviour based on patternBased on the historical data and identified patterns, the recommendation engine attempts to predict future behaviour, mainly what products the customer will purchase next.

Step 4: Recommend productThe recommendation engine will surface product recommendations based on its forecast of future behaviour.

Use

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Predicting next purchase based on historical behaviourVisit 1

Browse in-storeVisit 2

Browse onlineVisit 3

Buy in-storeVisit 4

Browse in-appVisit 5

Buy in-storeVisit 6

Browse online

Recommend product on next visit

Visit 7Buy online

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Collaborative-filtering

Collaborative-filtering – sometimes referred to as ‘look-alike modelling’ – segments users based on interests and behaviours, and then surfaces recommendations to one user based on the patterns of the group.

Step 1: Customers engage with site, make purchases online or in-storeCustomers engage with the site – visiting multiple pages and interacting with content – and make purchases online and in-store.

Step 2: Customers are segmented/clusteredBased on similar behaviours – purchases, pages visited, etc. – customers are segmented into groups.

Step 3: Customer views a product onlineA customer engages with the site – visiting multiple pages and interacting with content – and perhaps makes purchases online and/or in-store.

Step 4: Customer is segmented into a clusterWhen a customer is online, they are identified by the recommendation engine, as is the segment that they fall into. Depending on certain variables – e.g. when (time of day), where (at work or at home) or how (mobile or laptop) they access the site – they may be segmented differently during different touchpoints.

Step 5: Product recommendedBased on their defined segment, the recommendation engine will surface recommendations of products that index well for the defined segment (based on past purchases, content-based filtering, etc.).

Segment A Segment B

New customerDemo Geo Interests Purchases

Segment C

1. Define relationships

2. Recommendations based on purchase history

Recommending products based on look-alike modelling

New customerSegment C

Purc

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Recommend

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Predictive audience modelling

Predictive audience modelling is similar to collaborative-filtering. Where collaborative-filtering clusters users, however, predictive audience models ordinally rank products based on a predictive score of how likely a given audience is to purchase them.

Step 1: Customers engage with site, make purchases online or in-storeCustomers engage with the site – visiting multiple pages and interacting with content – and make purchases online and in store.

Step 2: Customers are segmented/clusteredBased on similar behaviours – purchases, pages visited, etc. – customers are segmented into groups.

Step 3: Based on customer behaviour, products are ordinally ranked based on likelihood of purchase by segmentLeveraging historic data, individual products are ordinally sorted (i.e. ranked) based on the likelihood of a customer from a defined segment purchasing that specific product.


Step 5: Customer is segmented into a clusterWhen a customer is online, they are identified by the recommendation engine, as is the segment that they fall into. Depending on certain variables – e.g. when (time of day), where (at work or at home) or how (mobile or laptop) they access the site – they may be segmented differently during different touchpoints.

Step 6: Product recommendedBased on their defined segment, the recommendation engine will surface recommendations of products that index well for the defined segment (based on past purchases, content-based filtering, etc.).

Recommend

Recommend

Recommendations based on purchase history

Segment AMost likely purchases

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Segment A customer previous purchases

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Predictive purchase modelling

Predictive purchase modelling uses advanced modelling techniques to analyse specific user data (demographics, etc.), user behaviour (site usage, third-party behavioural data, etc.), store data (website data, in-store purchase data, etc.) and other relevant variables, as well as the interactions between these variables, to assign prediction scores as to how likely a specific user is to purchase a product. It then surfaces recommendations for products which it determines the customer is most likely to buy.

Step 1: Data collectionWebsite usage data, online and in-store purchase data, interaction data, trends data and third-party data are aggregated.

Step 2: Interactions defined and weighted between variablesIndependent variables – including categories, keywords and tags – are defined, as are the relationships between them; however, in this advanced model, these definitions are dynamic and contingent upon multiple interactions. For example, time of day and access device may impact the defined relationship between apparel type, fit and colour.


Step 4: Data analysis; predictive score products based on likelihood of purchase by customerThe recommendation analyses the customer interaction and evaluates that data in the context of the other data points. From this analysis, individual products are ordinally sorted (i.e. ranked) based on the likelihood of that particular customer purchasing that specific product at that specific time.

Step 5: Product recommendedThe recommendation engine will surface product recommendations based on what it predicts to be the product that the customer is most likely to purchase at that given time. It means that the same shopper on the same site will get different recommendations if she is on her smartphone at home vs. her smartphone at work vs. her laptop at work. And from her smartphone at work, she may get different results depending upon the season, day of week, time of day or weather forecast for the next week.

Gartner predicts that by 2020, intelligent personalisation engines used to interpret customer intent will enable retailers to increase profits by as much as 15%.

“What’s Hot in Digital Commerce in 2017,” Gartner, 2017.

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The Modern Retailer’s Guide to Product Recommendations | Benefits and challenges

Menu

Chapters

03 How it works

02 Key concepts

05 Best practices


04

Benefits and challengesWe discuss the benefits that product recommendations can provide, as well as current challenges.



Better customer experienceProduct recommendations can greatly enhance the customer experience. Not only can they provide a more personal experience for the customer, they also add value by surfacing products that a customer may like, enable them to get more out of their past or current purchases with the addition of complementary accessories, and help them to discover new products and trends that they may not otherwise have seen.

Increased conversion ratesThere are two ways in which product recommendations have been shown to increase conversion rates. The first is through enhancing personalisation, which 75% of customers say makes them more likely to buy from a retailer.1 The second way is simply by surfacing more relevant product options for customers, options which customers are more likely to purchase. Conversion rates for visitors who arrived via a product recommendation were 5.5× higher than for other visitors.2

Increased average order valueBy surfacing related products and accessories, product recommendations help retailers with up-sell and cross-selling efforts, increasing average order value.

Improved inventory managementBrands can optimise product recommendations to help steer customers towards products that are not selling well. Through customer product recommendation data, businesses can gain visibility into the products that customers are currently interested in and which products they may purchase in the future. These insights can be leveraged in inventory forecasts.

Marketing insightsBy leveraging data from the recommendation engine, marketers can gain insights into product trends and products that customers are most likely to purchase. They can use this data to help set price points, inform emotions, inform product development and help create more personalised experiences for their customers.

Benefits and challenges

The Modern Retailer’s Guide to Product Recommendations | Benefits and challenges

1. Accenture Interactive, 2016.2. Barilliance survey, 2015.

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The Modern Retailer’s Guide to Product Recommendations | Best practices

Menu

Chapters


03 How it works

02 Key concepts

05 Best practices

05

Best practicesWe’ll look at some product recommendation best practices, as well as retail relevant use cases.



Best practicesSuggest accessories to products in cart“Try these socks with your new shoes.” “Don’t forget batter-ies for your remote.” “Find a mouse for your laptop.”

Add social proof with best-selling and top-rated items“Top selling portable speaker.” “Best selling refrigerator.” “Top rated running shoe in the store!”

Create product bundles and offer discounts“Customers frequently buy these items together.” “Buy the entire set and save 20%”

Recommend products through emailSend recommendations in marketing emails and provide discounts on related accessories in purchase confirmation emails.

Allow customers to find previously viewed itemsSave a user’s browsing history to make it easy for them to find a previously viewed item.

Recommend products for upcoming holidays and events“Prepare for Easter with these products.” “Don’t forget to buy champagne for New Year's Eve.”

Personalise recommendations based on past purchases“Since you already own this, you may also like this.” “Based on your past purchase, we thought you might like this.”

Adjust your recommendations to highlight specific itemsLeverage product recommendations to push top-selling (or under-selling) products.

Alert customer when a previously purchased or viewed product has been updated“This product has been updated.” “There is a newer version of this product.”

Never stop testingTest and continue to optimise your product recommendations to maximise performance and results.

In-store product recommendationsUsing mobile tools, in-store employees can access customer data and get data-driven personal recommendations to share with the customer.

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Common product recommendation messaging• Viewers of this product also viewed• Viewers of this product ultimately bought• You might also like• Recently viewed items• Trending in category• Top sellers• Customers also bought• Customers who bought this product also bought• Items viewed with items in your cart• Top sellers from your recent categories on homepage• Product accessories• If you like this, you might also like...• Recommended products• Products we love

Use cases Barilliance survey, Q2 2015.

Personalised Product Recommendation Type, Usage and Revenue

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Page where this recommendation appeared Product Site wide Category Cart Homepage

% of revenue from the recommendation type out of total revenue % of sites who use this recommendation type

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Disclaimer

© 2019 Microsoft. All rights reserved.

This document is provided ‘as-is’. Information and views expressed in this document, including URL and other internet website references, may change without notice. You bear the risk of using it. Some examples are for illustration only and are fictitious. No real association is intended or inferred. This document does not provide you with any legal rights to any intellectual property in any Microsoft product. You may copy and use this document for your internal, reference purposes.

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the modern retailer’s guide to product … › uploads › 2019 › 08 ›...

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