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Mobile-savvy shopper report

The impact of poor third-party app data on shopper behaviour

A report prepared by GS1 UK in conjunction with Cranfield School

of Management

January 2011

1. Executive summary

2. Background

3. Methodology

4. Results of the study

5. Why is third-party mobile app data so bad?

6. Recommendations for retailers, brand owners

and app developers

Contents



1,125product bar code scans

Only 9%products with correct

descriptions in third-party apps.

GS1 UK and Cranfield School of Management research looked at three, third-party generic smart phone applications (apps) providing additional information on products such as price comparison and nutritional data.

The research was restricted to third-party apps because, like GS1 standards, they are universal, i.e. store independent, and they are by far the most common form of scanning app.

Also, retailer specific bar code scanning apps use product data that is taken directly from brand owners which means that it is authentic and from a trusted source. GS1 UK’s research using a leading UK retailer’s bar code scanning app found that the description and image data was 100% accurate for the 375 products tested.

Unlike retailer specific apps, the source of third-party app product data is unclear. The research aimed to highlight the importance of product data quality in these generic apps and its impact on shopper behaviour.

Smart phone usage is growing fast. GS1 UK’s consumer research conducted by global market research specialist, TNS, shows that 1 in 3 (32%) consumers now have a smart phone. This goes hand in hand with the explosion in app development and usage. Over three billion mobile apps have been downloaded from the iPhone App Store and there is an emerging trend in utility apps that help 21st century ‘savvy shoppers’ make better informed choices before buying.

The key to making these services credible though is to provide accurate product information that consumers can trust. But the mobile-savvy shopper report raises serious questions about the ability of the current data infrastructure to support the growing development of apps that provide consumers with additional product information.

The research examined the quality of product descriptions and image data available in three third-party, generic apps. 375 grocery products were selected at random and scanned using the three generic apps. GS1 UK and Cranfield School of Management were then able to rate the accuracy of data available to the apps by comparing it to data authorised by the brand owner.

From a total of 1,125 scans across all three apps, only 9% of scans returned the correct product description when compared with the brand owner’s approved data.

A staggering 75% of scans returned absolutely no product information at all and 87% of scans returned no image. But when information was returned the story did not get any better with product information being wrong 1 in 5 times.

This lack of accurate and trustworthy product data is having a significant impact on consumers. 50% of consumers claimed that they needed an accurate description and image to feel confident that the product is the right one. And 34% of consumers won’t buy the product at all without confidence in the data.

And it is bad news for app developers too given the lack of data available. Over 1 in 4 (27%) respondents claimed that if they could not find the products they were looking for they would stop using the app altogether.

So what can be done about the issue? The report states that crowd or multiple sourcing techniques frequently produce poor quality product data and can therefore be identified as the cause of bad mobile data. Therefore, it is strongly recommended that industry accesses product information from a reliable source that can be trusted.

1 Executive summary

100%products with correct

descriptions and images in leading UK retailer app


The impact of poor third-party app data on shopper behaviour The impact of poor third-party app data on shopper behaviour

2 Background

Markets around the world are witnessing the emergence of a new type of consumer, the 21st century ‘savvy shopper’. These consumers are smarter, more astute and well-informed. They consistently benchmark to find the best deal and to meet specific requirements. And they are cross-channel shoppers who use a range of methods to access information and buy products. For example, a consumer may research a product online, purchase it using a smart phone and pick it up in store.

This new approach to shopping brings with it a growing demand for more and better product information. Industry trends show that demand for more product information is significantly increasing. GS1 UK’s Data Crunch Report forecasted that this demand would grow by 400% in the next five years. Consumers now and in the future will expect more information from product packaging. On grocery products many consumers expect to see nutritional information, allergy notifications and whether or not the product conforms to certain lifestyle choices. Before they

purchase electrical products, a large number of consumers want to compare technical specifications, performance, read reviews and compare prices.

Historically, shoppers have had a tangible product which they can take off the shelf and use their primary senses to evaluate. Savvy shopping habits mean the consumer can now make key buying decisions remote from the physical product. They will therefore be less prone to marketing techniques such as shelf placement, packaging graphics and branding, but they will also be accessing core product information from numerous sources, not simply from the back of pack or the brand owner.

Much of this purchasing behaviour existed before the digital age, but modern technology such as the smart phone, makes this information much more accessible.There are a growing number of mobile apps available to smart phone users in the UK.

75%products with no information at all

375grocery products

tested Source: Channel AdvisorSales by different internet channels 2000 - 2009

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

75%

10%

10%

5%

20%

26%

41%

13%

18%

24%

43%

15%

9%

28%

43%

13%5%

2%

2000 2007 2008 2009

Direct to website sales

Marketplace (e.g. ebay, Amazon)

Paid Search (e.g. Google Ads)

Comparison Shopping Engines (CSEs) (e.g. Kelkoo or Google products)

Mobile phones

Social (e.g. Facebook)

The impact of poor third-party app data on shopper behaviourThe impact of poor third-party app data on shopper behaviour The impact of poor third-party app data on shopper behaviour

Many of these enable consumers to search for additional product information which has led to a significant increase in purchases being made using mobile phones. Recent research found that product sales on mobile devices now constitute 5% of all internet sales.*

Smart phone users also tend to be younger consumers who are more inclined to be savvy shoppers. GS1 UK’s research found that the majority of smart phone users were aged between 16 and 44. But clearly, in the next decade this group will become older, but continue to use smart phone technology and it will become the norm increasing the number of savvy shoppers.

Initially, scanning apps were used predominantly for price comparison purposes. With a greater demand for smart phones and mobile apps, comes a greater demand for functionality beyond just comparing prices. Due to the demand from consumers for additional information, there has been an increasing trend towards providing additional functionality such as ingredient and environmental data.

GS1 UK wanted to find out more about the scale of the data quality issue and the impact of missing or wrong product data on consumer behaviour. The research findings provide an insight into what needs to be addressed for industry to leverage the opportunities presented by the mobile phone phenomenon. GS1 UK has used this comprehensive research to outline concrete recommendations about how retailers, brand owners and app developers can address the issue.

Smart phone owners by age group

*Channel Advisor, 10 multichannel strategies to boost sales, 2010 - http://www.channeladvisor.co.uk/downloads/christmas-strategies.pdf

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

16-24 25-34 35-44 45-54 55-64

50%


3

Qualitative and quantitative research were used to understand the scale of poor product data on third-party mobile apps and its impact on shopper behaviour. To ensure robustness, GS1 UK worked with Cranfield School of Management and identified three different research approaches:

• The physical measurement of the quality of data that is currently available on mobile scanning apps• A TNS national survey of consumers to understand their perceptions of mobile data • The use of secondary sources of information about the current trends and opportunities in the mobile commerce industry. Measuringdataqualityonthird-partymobilescanningapps

Three apps were chosen based on the iPhone app ranking website www.topappcharts.com which provides a ranking of the most downloaded apps. The vast majority of bar code scanning apps are classified as utilities. Utilities apps are typically used for price comparison,

Methodology

but can include other functionality. As such two apps from this category were tested. However in the health and fitness category, bar code scanning apps which provide additional nutrition information on products are also emerging. For this reason, the top downloaded bar code scanning app for food nutrition was also tested.

Thefullcriteriafortheappstested

• Top downloaded paid for utility bar code scanning app• Top downloaded free utility bar code scanning app • Top downloaded health and fitness bar code scanning app

A random selection of 375 grocery products was scanned using these three bar code scanning apps. The aim of the exercise was to replicate how a consumer would scan products in a supermarket using a smart phone.

The product data and image returned on the generic app for each scan was compared to the brand owner approved description and image held on GS1 UK’s TrueSource Product Catalogue.


The information returned in the apps was then systematically categorised based on criteria relating to the content of the data displayed. These categories were as follows:

• Missing data - If there was no data displayed at all then it was classified as missing.• Wrong data – Descriptions and images that did not represent the product being scanned in any way were classified as wrong.• Partially correct data – Data that did not match the brand owner approved descriptions and images were partially correct.• Correct data – If a description or image matched the brand owner approved data then it was classified as correct.

Nationalsurveyofconsumerperceptions

In addition, detailed insight of consumer perceptions was gained through research conducted by TNS on the opinions of 1086 consumers. The survey was designed to capture the opinions of consumers when faced with inconsistent data while

shopping using their smart phones.The survey questions were framed around a hypothetical app that scans bar codes and returns basic product information and its associated image. A shopping environment scenario was created with close proximity to the point-of-sale so that the perceptions of non-smart phone users could also be captured.

The survey, conducted in October 2010, included 29 questions on ingredient, environmental and price comparison information which were framed around the following themes:

• Service concept: Does the consumer like the mobile scanning app concept versus will the consumer actually use the product?• Consumer trust: How important is it that the information returned by the app is from a trustworthy source?• Accuracy of data: Does accurate product information really matter to consumers?• Impact on apps: What are the implications for app developers if the data returned by their app is missing, incomplete or wrong?• Impact on purchasing decision: How does inaccurate product data on mobile apps affect shopper behaviour?


Veryfewthird-partyproductscansreturncorrectdata

The research carried out on the quality of data provided by the tested third-party mobile apps revealed that on average, only 9% of the products scanned returned the correct basic description. A shocking 75% of products had no basic data available at all.

In the case of the product image, 87% of the products did not have any available for users to view. The level of missing and inaccurate product image data was as high as the basic description returned by all three of the tested mobile bar code scanning apps.

With the increase in consumer demand for more product information and the emergence of more apps providing data such as nutrition, allergy and environmental, it is difficult to see how app developers will cope in the future with the quality of their data when GS1 UK’s research shows that even basic product and image information is very poor or even non-existent.

The research also highlighted that there is limited information about UK products available to the mobile apps that were tested. The increasing trend of shoppers using mobile scanning apps creates a strong business case for UK brand owners to ensure that their products are available through this channel. Most importantly, the product information made available should be trusted, accurate and up-to-date.

Wrongdatafuelspoordataintegrity

The data quality research also found significant issues with the integrity of the data available on mobile scanning apps. On average, the research found that over 16% of the description data available was partially wrong or completely wrong meaning that it is either missing key information or it contains text that in no way relates to that product.

These inconsistencies suggest that the current methods used to source product data are failing in as many as one in five instances.

Only 9% of products scanned with third-party apps had correct descriptions

4 Results of the study

Overview of description data for all tested apps

Overview of image data for all tested apps

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

12%

87%

1%CorrectData

NoData

WrongData

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

9%PartialData

9%CorrectData

75%

NoData

7%WrongData


Overview of image data for all tested apps

that 66.2% of it is inaccurate which means that it does not match the brand owner’s description. With app’s B and C, there is only a 1 in 10 chance of returning any description at all.

The implications of this are that as more data becomes available for mobile apps to use, there’s a higher likelihood of it being more inaccurate. With more mobile apps being created every day to address the demand for more information from consumers, there is a clear need for industry to start improving its data quality to stop it from being an even bigger problem to solve in the future.

Once again, with the increasing trend of consumers relying on mobile scanning apps to provide them with data that is significant to their lives such as allergic and health information, it is vital that the integrity of data available to such shoppers is addressed promptly to avoid any unfortunate outcomes.

Themoredataavailable,thegreatertheinaccuracies

The results of the research also suggest that the data quality problem is exacerbated when more product information is available. The app with the highest amount of data available to it (app A) also had the highest percentage of incorrect data.

Even though it had considerably more data available, the research shows that users of app A still only have a 55.2% chance of having any data returned at all. Where data is available, the research found

Data quality found in tested mobile apps

50% of consumers need accurate description & image to feel confident about product 100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

19.5%

17.1%

18.7%

44.8% 85.1%

5.9%7.7%

95.2%

1.3%1.1%2.9%0.8%

App A App B App C

No Data

Correct Data

Partial Data

Wrong Data


Consumerconfidenceaffectedbyaccuracyofproductdata

The consumer perception research explored the impact of data integrity on shopper behaviour.

When 1,086 consumers were asked what type of information was needed for them to be sure that what they were reading on their mobile screens was actually about the product they were holding in their hands, over 50% claimed that they needed an accurate description and image to feel confident that the product is the right one.

This suggests that unless there is an accurate description and picture accompanying the price, ingredients or environmental data, app developers risk alienating over half of their potential customers.

These findings are hugely significant when only 9% of the sample products that were scanned during the data quality research returned correct descriptions – a 9 in 10 chance that the information is missing, wrong or partially correct.

Consumerswon’tuseappagainifdataiswrongormissing

One in three consumers surveyed said that they would not use the mobile scanning app again if the data returned was wrong or missing. 27% of the respondents also claimed that if they could not find the products they were looking for they would stop using the app altogether.

The figure rises significantly if the data returned is about another product or is unrelated to the product they are holding in their hands. In this case, an average of 37% of the respondents said they would stop using the app. Respondents that were asked specifically about ingredients data were most concerned with 43% claiming that they would not use the app again.

These findings suggest that consumers are very sensitive to poor quality product data, to such an extent that they will stop using the app altogether.

37% of consumers won’t use app again if wrong information shown

**Exploring the third dimension report, Coca-Cola Retailing Research Council, 2009

Yes

Don’t Know

No

16%

46% 37%

Would you use the app again if it retrieved the wrong information?


Don’t Know

No

Shopperswon’tbuytheproductiftheycan’ttrustthedata

The research found that consumer confidence and trust in product data strongly impacts their purchasing decisions. One in three shoppers said that they would not buy a product if they could not trust the accuracy of the data.

The findings strongly indicate that consumers’ willingness to use mobile apps is greatly reduced if they don’t have confidence in the data. The survey found that without an accurate product description and a trustworthy source, consumers cannot be confident in the price, ingredient and environmental information that they are requesting from the app. Without confidence in the data, 34% of consumers won’t buy the product at all.

Consumers need particular pieces of information to enable them to clearly identify the product that they are researching before they decide to make a purchase. The research clearly shows that there is a need

for this information to be provided by a trustworthy source with two thirds of the consumers surveyed demanding that they be able to trust the source of the data.

Differentconsumergroupshavedifferentinformationneeds

The consumer research also found some interesting differences in information needs between consumer groups with varied shopper habits. People with dietary conditions need to trust ingredients description and image information more than those without dietary conditions. This is a very important point especially when 40% of shoppers look to meet specific dietary needs either for themselves or for someone they are shopping for.**

This increasing trend means that there is growing demand for dietary information and a specific need to trust the data provided even more because of its significance to the lives of people with dietary conditions.

34% of consumers won’t buy product if app data is inaccurate

**Exploring the third dimension report, Coca-Cola Retailing Research Council, 2009

Yes

Don’t know

No

49%

17%

34%

Would you still purchase the product if you don’t trust the data?


5 Why is third-party mobile app data so bad?

The research suggests that the quality of grocery data in mobile scanning apps is very poor and that consumers need to trust the information provided by third-party apps if they are going to use them to research and purchase products.

The findings also highlight that where there is more data, a greater proportion of it is wrong. It is important to explore why this is happening and why the problem is getting worse. A large number of these failings can be attributed to how data is collected.

Crowdsourcing

Crowd sourcing is a technique that allows anyone to contribute to a pool of information. Crowd sourcing is a wholly appropriate technique for gathering opinions, reviews and other subjective information. It works well for websites such as TripAdvisor where opinion is important or sites such as Wikipedia that are peer reviewed and organic.

But often product data is absolute and unquestionable, such as size, weight or ingredients. As such one would expect this data to come direct from the brand owner, but it appears not to be the case. Data is

being collected, distributed and presented in generic mobile apps to consumers from a variety of unverified sources. It is not being authenticated by brand owners who are losing control of vital product information on which consumers are basing their purchasing decisions.

Differentproductdescriptionsforthesameproduct

A search for a product on a third-party mobile app, such as those tested in this research, often brings up many versions of the same product with slightly different descriptions. This occurs when sellers enter their own version of the product description into their systems. Slight variations in data entry into these systems result in multiple search results for the same product when scanned. For example, one business may enter a bottle of whisky onto their system as ‘Jim’s 10 year old Scottish malt whisky 70cl’, while another might refer to it as ‘10 yo Scottish whisky by Jim 700ml’.

These inconsistencies often confuse consumers and as GS1 UK’s research shows, if their confidence in the product data is undermined, it makes them less inclined to purchase the product.


The research highlights how poor product data in third-party mobile apps can not only erode consumer confidence, but also affect their purchasing decisions. A failure to address the issue of poor product data will damage the credibility of companies who want to interact with consumers through marketing channels such as the web and mobile. This will ultimately damage sales and overall profitability. The longer industry fails to address this problem, the worse the problem will get.

Whereshouldthedatacomefrom?

As the research highlights, where product information is gathered from a variety of different formats from numerous sources, it produces poor quality mobile data which in turn confuses the consumer. This research has also shown that if consumers are confused by the data then it has a significant impact on their shopping behaviour and the likelihood of them buying the product.

Therefore, it is strongly recommended that industry follow the lead of major UK retailers and accesses product information that has been authenticated and comes from a trusted and reliable source.

The onus is on brand owners and app developers to ensure that their product information comes from a trusted source. Brand owners who want to take advantage of the growing mobile-savvy shopper market, need to have a process in place to ensure that their data is kept up-to-date and accurate, and that it is made available to app developers and retailers. As shown in the research, this will affect consumer behaviour which will ultimately impact sales.

As an independent industry body, GS1 UK recommends that product data sourced from, or authenticated by, the brand owner is best practice for mobile apps. GS1 UK has launched the TrueSource service to support industry in managing accurate and trusted product data in preparation

for sharing with trading partners through the Global Data Synchronisation Network (GDSN).

Anindustrywidesolution

The issues that have been highlighted in this research have implications for all companies involved in consumer markets. With the trend of mobile usage growing, industry needs to address the issue of data quality in mobile scanning apps as a priority before it gets worse.

It is highly recommended that an industry initiative to improve the quality of generic mobile app data should be set up to address the issues raised in the research and leverage the potential of the mobile app market. As a not-for-profit organisation with a strong reputation for bringing companies together to address industry-wide issues, GS1 is best placed to facilitate this type of initiative.

Brand owners, app developers and retailers are strongly encouraged to take part in this process.

6 Recommendations for retailers, brand owners and app developers


AcknowledgementsWe would like to acknowledge the contributions of the GS1 UK and Cranfield School of Management team that worked on the report.

This report was produced in conjunction with Cranfield School of Management as part of the ongoing strategic research partnership between GS1 UK and Cranfield University.

Report authors: Oliver Coussins, Tom Beston, Suraya Adnan-Ariffin, Robert Griffiths and Dr. Silvia Rossi.Report researchers: Oliver Coussins and Dr. Silvia Rossi.

Published by GS1 UK (London, WC1V 7QH and Cranfield School of Management Bedford, MK43 0AL)


ContactGS1UKGS1 UK has driven innovation in the supply chain for over thirty years. It is part of the global GS1 organisation, dedicated to the development and implementation of global data standards and solutions for the supply chain. The GS1 System is the most widely used supply chain standards system in the world. GS1 UK helps industry to implement these data standards through the use of bar codes, Radio Frequency Identification (RFID), Global Data Synchronisation (GDS) and electronic business messaging.

Contact GS1 UK

Staple Court11 Staple Inn BuildingsLondonWC1V 7QH

T +44 (0)20 7092 3500F +44 (0)20 7681 2290E [email protected] www.gs1uk.org

Copyright GS1 UK 2010

GS1 UKStaple Court11 Staple Inn BuildingsLondonWC1V 7QH

T +44 (0)20 7092 3500F +44 (0)20 7681 2290E [email protected] www.gs1uk.org5 012345 000756 >

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