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How Marketers Are Using Machine Learning To Cross-Sell And Upsell

March 16, 2017 at 2:22 PM

McDonalds mastered the upsell with one simple question at the time of purchase: “You want fries with that?”. A simple and relevant question at the right time that has likely generated millions of extra dollars in revenue through the years for the company. Ever since then, companies have tried to emulate their success by identifying complementary products in their offering and training sales staff to ask customers the right question at the right time.

Today, the generation and tracking of customer data, transaction data and purchase behaviour data are enabling companies to move away from a generic upsell and cross-sell to a personalised one, and machine learning is ensuring data-driven recommendations reach the right customer at the right time.

In this blog post, we look at how Amazon and Hyatt Hotels are using machine learning to improve upsell and cross-sell effectiveness and personalise their approach.

Amazon’s machine learning knows what you “might like”

Amazon, a veritable cornerstone of the online world, still manages to not only on-board new business at an impressive pace, but continues to demand a large share of wallet of existing customers with targeted, data-driven cross- and up-selling strategies thanks to machine learning.

Not 100% clear on what Machine Learning is? Read our layman's explanation.

Using data correlation techniques on its massive database of over 150 million customers, Amazon is able to gain insights on past purchases, reviews, customer preferences and product popularity  to make relevant and personalised recommendations to users that match their buying history and preferences. On every product page, Amazon is using machine learning to display products other customers bought when they bought the product you’re viewing. By analysing purchase behaviour and identifying patterns, Amazon identifies which products are often bought together and displays the complementary product at the critical moment that can trigger an impulse purchase. For Amazon, their use of machine learning increases share of wallet and total size of purchase, as well as improves the customer experience through the relevance of their recommendations.

Read our blog post on how machine learning is changing the way a major retailer changes shopping behaviour.

Hyatt Hotels’ predictive analytics boosts revenues

In March this year, the Hyatt Group announced that it had aligned its operations to use predictive analytics to improve cross- and up-selling to guests at their 500-plus hotels across the globe.

By analysing guest history and preferences and comparing them to those of guests with similar profiles, Hyatt is able to automatically display relevant messages that tell desk agents that the guest they are checking in is likely to want to upgrade their room to one with a view, or might want to know more about amenities the hotel offers. It’s very similar to Amazon’s “you might like this product” which Hyatt admits was their inspiration for this new data-driven step in the guest check-in process.

According to the group’s SVP for Strategy and Analysis, Chris Brogan, In 2014 in the Americas, we rolled out a program that has increased the average incremental room revenue, post-reservation, by 60%, 2014 versus 2013. That’s compared with similar programs in the past that lacked the sophisticated analytics.” Among Hyatt’s chief data sources was its membership program that gave the group the per-individual insights they needed to offer special discounts or amenities, based on a particular member’s past traveling, accommodation and other preferences. The success of the group’s initial foray into big data in the Americas has led to the decision to adopt the predictive model on a global scale.

Download our guide to using machine learning in business, where we explore how you can use machine learning to better tap into your business data and gain valuable, informing insights to improve your business revenue. 

Learning from every new byte of data

As people’s lives go through their natural ebbs and flows, their requirements and expectations are subject to change, and one of the biggest mistakes any business can make is to assume that this never happens for their customers. (Click to Tweet!)

The beauty of machine learning is that it is constantly learning from new data and improving its predictive ability with every new byte of data it’s fed. Analysis of historic data can map changes in behaviour back to new recommendations. Knowing and predicting what your customers may want – even before they know it themselves - and meeting that need at the right moment is the competitive-edge machine learning and predictive technologies can provide.

Read our media coverage to learn how we applied the same predictive analytics techniques we use to predict consumer behavior for our clients to predict the 2016 Oscar winners, as well as the results of all of the 2015 Rugby World Cup matches with a 91% rate of accuracy.

Using machine learning in business - download guide

Julian Diaz
Julian Diaz
Julian Diaz was Head of Marketing for Principa until 2017, after which he became Head of Marketing for Honeybee CRM. American born and raised, Julian has worked in the IT industry for over 20 years. Having begun his career at a major software company in Germany, Julian made the move to South Africa in 1998 when he joined Dimension Data and later MWEB (leading South African ISP). Since then, Julian has helped launch various South African technology brands into international markets, including Principa.

Latest Posts

The 7 types of credit risk in SME lending

  It is common knowledge in the industry that the credit risk assessment of a consumer applying for credit is far less complex than that of a business that is applying for credit. Why is this the case? Simply put, consumers are usually very similar in their requirements and risks (homogenous) whilst businesses have far more varying risk elements (heterogenous). In this blog we will look at all the different risk elements within a business (here SME) credit application. These are: Risk of proprietors Risk of business Reason for loan Financial ratios Size of loan Risk industry Risk of region Before we delve into this list, it is worth noting that all of these factors need to be deployable as assessment tools within your originations system so it is key that you ensure your system can manage them. If you are on the look out for a loans origination system, then look no further than Principa’s AppSmart. If you are looking for a decision engine to manage your scorecards, policy rules and terms of business then take a look at our DecisionSmart business rules engine. AppSmart and DecisionSmart are part of Principa’s FinSmart Universe allowing for effective credit management across the customer life-cycle.  The different risk elements within a business credit application 1) Risk of proprietors For smaller organisations the risk of the business is inextricably linked to the financial well-being of the proprietors. How small is small? The rule of thumb is companies with up to two to three proprietors should have their proprietors assessed for risk too. This fits in with the SME segment. What data should be looked at? Generally in countries with mature credit bureaux, credit data is looked at including the score (there is normally a score cut-off) and then negative information such as the existence of judgements or defaults; these are typically used within policy rules. Those businesses with proprietors with excessive numbers of “negatives” may be disqualified from the loan application. Some credit bureaux offer a score of an individual based on the performance of all the businesses with which they are associated. This can also be useful in the credit risk assessment process. Another innovation being adopted internationally is the use of psychometrics in credit evaluation of the proprietors. To find out more about adopting credit scoring, read our blog on how to adopt credit scoring.   2) Risk of business The risk of the business should be managed through both scores and policy rules. Lenders will look at information such as the age of company, the experience of directors and the size of company etc. within a score. Alternatively, many lenders utilise the business score offered by credit bureaux. These scores are typically not as strong as consumer scores as the underlying data is limited and sometimes problematic. For example, large successful organisations may have judgements registered against their name which, unlike for consumers, is not necessarily a direct indication of the inability to service debt.   3) Reason for loan The reason for a loan is used more widely in business lending as opposed to unsecured consumer lending. Venture capital, working capital, invoice discounting and bridging finance are just some of many types of loan/facilities available and lenders need to equip themselves with the ability to manage each of these customer types whether it is within originations or collections. Prudent lenders venturing into the SME space for the first time often focus on one or two of these loan types and then expand later – as the operational implication for each type of loan is complex. 4) Financial ratios Financial ratios are core to commercial credit risk assessment. The main challenge here is to ensure that reliable financials are available from the customer. Small businesses may not be audited and thus the financials may be less trustworthy.   Financial ratios can be divided into four categories: Profitability Leverage Coverage Liquidity Profitability can be further divided into margin ratios and return ratios. Lenders are frequently interested in gross profit margins; this is normally explicit on the income statement. The EBIDTA margin and operating profit margins are also used as well as return ratios such as return on assets, return on equity and risk-adjusted-returns. Leverage ratios are useful to lenders as they reflect the portion of the business that is financed by debt. Lower leverage ratios indicate stability. Leverage ratios assessed often incorporate debt-to-asset, debt-to-equity and asset-to-equity. Coverage ratios indicate the coverage that income or assets provide for the servicing of debt or interest expenses. The higher the coverage ratio the better it is for the lender. Coverage ratios are worked out considering the loan/facility that is being applied for. Finally, liquidity ratios indicate the ability for a company to convert its assets into cash. There are a variety of ratios used here. The current ratio is simply the ratio of assets to liabilities. The quick ratio is the ability for the business to pay its current debts off with readily available assets. The higher the liquidity ratios the better. Ratios are used both within credit scorecards as well as within policy rules. You can read more about these ratios here. 5) Size of loan When assessing credit risk for a consumer, the risk of the consumer does not normally change with the change of loan amount or facility (subject to the consumer passing affordability criteria). With business loans, loan amounts can range quite dramatically, and the risk of the applicant is normally tied to the loan amount requested. The loan/facility amount will of course change the ratios (mentioned in the last section) which could affect a positive/negative outcome. The outcome of the loan application is usually directly linked to a loan amount and any marked change to this loan amount would change the risk profile of the application.   6) Risk of industry The risk of an industry in which the SME operates can have a strong deterministic relationship with the entity being able to service the debt. Some lenders use this and those who do not normally identify this as a missing element in their risk assessment process. The identification of industry is always important. If you are in manufacturing, but your clients are the mines, then you are perhaps better identified as operating in mining as opposed to manufacturing. Most lenders who assess industry, will periodically rule out certain industries and perhaps also incorporate industry within their scorecard. Others take a more scientific approach. In the graph below the performance of an industry is tracked for two years and then projected over the next 6 months; this is then compared to the country’s GDP. As the industry appears to track above the projected GDP, a positive outlook is given to this applicant and this may affect them favourably in the credit application.                   7) Risk of Region   The last area of assessment is risk of region. Of the seven, this one is used the least. Here businesses,  either on book or on the bureau, are assessed against their geo-code. Each geo-code is clustered, and the projected outlook is given as positive, static or negative. As with industry this can be used within the assessment process as a policy rule or within a scorecard.   Bringing the seven risk categories together in a risk assessment These seven risk assessment categories are all important in the risk assessment process. How you bring it all together is critical. If you would like to discuss your SME evaluation challenges or find out more about what we offer in credit management software (like AppSmart and DecisionSmart), get in touch with us here.

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