Introduction
In today’s connected world, the importance of serving the right content, the right products, and the right experience at the right time has never been more important. Not only is this crucial for driving customer engagement with a retailer’s brand and products, but it is necessary in order to continuously improve brand messaging, product offerings, and experiences across channels. In order to effectively execute upon these needs, marketers, merchants, suppliers, brand managers, etc. need the tools to gather and analyze consumer and product intelligence data, and they need the tools to be able to execute upon that analysis. RevTech looks for startups that have reliable, intelligent data analytics solutions as well as retail and hospitality-specific applications of this intelligence to improve cross-channel customer engagement and personalized customer experiences, marketing and content offerings, and product assortments. End-to-end solutions are especially interesting to us.
Critical components of Shopper Intelligence and Personalization solutions include AI (including Machine Learning, Natural Language Processing, Image Recognition, and Computer Vision), Internet of Things (IoT), Predictive Analytics, Marketing / Advertising Technology, and Personalization Platforms, and may include offerings across hardware, software, and services.
Global Landscape
Market Size & Growth
The overall global AI solution market across IoT, data capture and integration, predictive analytics, digital personal assistants, and AI services is predicted to be $29.9B by 2022, growing at 30.8% CAGR 2017-2022. [1] The AI solution market in the Retail & Consumer Goods industry is expected to be about $2.65B by 2022 (excludes Digital Personal Assistant solutions), with about half of the value coming from AI predictive analytics solutions in Retail & Consumer Goods. [2]
AI Predictive Analytics in the Customer Intelligence space (across all industries, not just Retail & Consumer Goods) is expected to be nearly $3B by 2022. [3]
Messenger Bots are expected to generate almost $600M in revenue by 2022 across all industries. [4]
In 2017, BCG predicted that $800B of advertising spend across Retail, Healthcare, and Financial Services would be allocated to personalization by 2022. [5]
Finally, the global artificial intelligence-based personalization market is expected to grow at a CAGR of close to 13% during the period 2018-2022, according to Technavio’s latest market research report (refer to infographic in Figure 1). [6]
Figure 1
Based on these analyses, we see the Shopper Intelligence and Personalization market as a multibillion-dollar opportunity in Retail. Furthermore, this opportunity will continue to grow as technology continues to improve and viable solutions develop further.
Market Trends & Customer Needs
Personalization has use cases that span the customer journey, for example targeting and converting new customers versus driving loyalty and increasing spend of existing customers. Additionally, leveraging shopper intelligence and personalization is applicable across functions – marketing, merchandising, operations, etc. This is because consumers are looking for increasingly personalized products, content, and experiences across all channels, physical or digital.
In-Store Personalization
A recent Oliver Wyman report found that, in China, experience and quality are major differentiators for consumers, especially middle- and upper-class consumers shopping in physical stores. This report found that 38% of survey respondents wanted “personalized clothing suggestions” in a physical store setting. [7] Additionally, the trend of in-store personalized experiences is not just happening in China. A recent McKinsey report stated,
“…that “offline” person-to-person experiences will be the next horizon for personalization. Some 44 percent of CMOs say that frontline employees will rely on insights from advanced analytics to provide a personalized offering; 40 percent say that personal shoppers will use AI-enabled tools to improve service; and 37 percent say that facial recognition, location recognition, and biometric sensors will become more widely used.” [8]
However, the same McKinsey report also found that less than 10% of the companies they surveyed leverage personalization beyond digital channels despite proven success in incremental sales lift and increased basket size when deployed to offline channels effectively.
Marketing Spend Efficiency
For retailers and marketers, a main driver behind the desire to leverage customer insights and personalization is the desire to optimize marketing spend. Targeted communications and product recommendations can lead to 10-30% increases in marketing spend efficiency. [9] Personalization can also help to reduce acquisition costs by as much as 50% [10], which is especially important in a time when customer acquisition costs and digital marketing spend are reaching an all-time high.
Consumers readily admit that they are more willing to share personal data in order to have a more personalized experience, and that they are more likely to engage with brands that customize messaging to their interests [11]. However, marketers struggle significantly with executing data-driven personalization – 63% of respondents of a recent survey indicated that this was the most difficult tactic to execute above email marketing, SEO, and other activities. [12]
Relevant Product Assortment
As important as it is to have relevant marketing content, it is also crucial to have relevant product offerings in order to drive conversion. This can be two-fold – either providing personalized product recommendations based on a retailer’s existing assortment or providing customized products to customers that are somehow tailored to them (e.g. apparel custom fitted to their body). Deploying product recommendations combined with relevant, triggered communications can result in a 5 to 15% increase in revenue. [13] Additionally, 47% of customers say that if product recommendations are not tailored to them from a retailer directly, they will go to Amazon instead. [14]
“Mass customization” of products themselves is a growing trend. Technologies such as AI, 3D imaging, robotics, and high-speed data processing have helped accelerate this trend. [15] Not only does this allow consumers to have highly custom products made for them, but it provides manufacturers with important market data and feedback that can further help with product development.
Other Trends & Considerations
Shopper intelligence and personalization can be combined with other technologies to enhance the customer experience. For example:
“AI integration with augmented reality is one of the major trends being witnessed in the global artificial intelligence-based personalization market. In the retail industry, several brands are using a graphic illustrator that allows their target customers to try on glass frames and cosmetic products including lipsticks, eyeshadows, and other products through the company’s app or website or both. The graphics illustrator can access and identify facial features and then use augmented reality to apply the product to the desired body part.” [16]
Additionally, because customers may interact with different providers across their exploration, decision-making, purchasing, and post-purchase stages of their journey, creating connection points across stages and providers (e.g. stores, brands, media channels, etc.) can elevate the personalization opportunity. Creating a “personalization ecosystem” is the next step in providing seamless experiences at each stage of the journey. [17]
Retailer Interest
Because of the increased development of technology to support consumer intelligence gathering and personalization as well as the complexity involved with implementing these solutions, the interest from retailers is quite strong in this space, making it an attractive market for startups than can provide reliable solutions. A recent survey found that 79% of B2C marketers in retail are investing in personalization tools, which is more than any other industry. [18] In the next three years, retailers are expected to increase investment in personalization from 0.7% of revenues to 18%, with that number reaching up to 30% for retailers that currently have more advanced personalization capabilities already. [19]
Challenges Faced by Retailers
Key issues retailers face regarding Shopper Intelligence & Personalization that represent opportunities for viable solution providers are:
- “Bad” / poor quality data
- Lack of data analysis
- Customer churn
Poor Quality Data
Harvard Business Review indicates that 60% of data analysts’ time is spent cleaning and organizing data, while 50% of their time is wasted on analyzing “bad” data. [20] This not only leads to unoptimized use of time, but potentially to misguided strategies, misaligned priorities, and failed implementation of business intelligence initiatives. Furthermore, HBR estimates that the cost of poor-quality data in the US across all industries is $3.1T. [21] Solutions targeted at streamlining, cleaning, and organizing data collection can result in labor cost savings and cost savings associated with more efficient business initiatives.
Lack of Data Analysis
Retailers often have copious amounts of data, but either are not able to or simply do not analyze this data. A UT study found that companies could increase profits by $2 billion per year by making use of 10% of available data. [22] Solutions that can more effectively analyze data, especially those that can provide meaningful, actionable and predictive insights, can result in cost savings and revenue lifts.
Customer Churn
US companies lose $135.8B per year as a result of customer churn. [23] Customers often churn due to poor experience. This is problematic as customer acquisition costs continue to rise, making existing customers even more valuable. Providing a better, more curated experience is shown to reduce churn. Therefore, solutions that can personalize customer experiences can directly positively impact customer retention, customer lifetime value, and revenue.
Major Players
Main Customers
Customer intelligence and personalization is a cross-industry trend that major companies such as Coca-Cola, Fabletics, Netflix, Sephora and USAA, among others (89% of digital businesses, in fact) are investing in. [24] Other notable retail brands investing in personalization include Nike, Asos, and Graze. [25] Other players such as Macy’s are also investing in personalization and shopper intelligence through building up their loyalty programs and digital capabilities. The interest in personalization tools for major brands and retailers across the board is quite strong.
Incumbents: Large Tech Players
- Recently purchased three AI startups; 1) MetaMind for personalization and customer support solutions, 2) PredictionIO for machine learning, and 3) Tempo AI, a smart calendar [26]
- Salesforce Einstein is a “smart CRM” assistant that can be leveraged for the Salesforce Customer 360 Platform
- Allows for personalizing customer engagement through conversational AI
- Provides a “real-time customer profile” based on AI-driven analytics
- Offers end-to-end solution from insights to content to engagement to commerce
- Has proprietary predictive algorithm for product recommendations and is now offering AWS-based personalization solution leveraging machine learning to B2B customers
- Current partners and customers include Subway and Domino’s, among others
- Has visual product search and recommendations AI for online customer engagement
- Tools for personalized digital marketing
New Entrants [27]
In-Store & Omni-Channel Solutions
- Description: Retail analytics company focused on providing real-time analytics to brick & mortar retailers
- Series E3 ($25M) raised in March 2016
- Description: Offers an integrated solution that leverages AI and analytics to crate personalized and automated experiences across the customer journey and across channels (can leverage in-store data)
- 2nd Round raised ($12.28M) in 2015
- Description: Analytics technology designed to help retail teams to capture lost sales and improve customer experiences. The company’s technology applies patented machine learning algorithms to pre-existing point of sales data and captures lost sales by correcting operational inefficiencies on a store level
- Series B raised ($16M) in March 2019
Marketing & Branding-Focused Solutions
- Description: AI engine that leverages smartphone sensor data to develop customer insights and help retailers deliver personalized offers and rewards to customers as well as optimize marketing spend
- Series C raised ($10M) in March 2020
- Description: Developer of a mobile advertising platform designed to engage brands with consumers through personalized advertising and video experiences
- Late Stage VC – 5th round raised in Jan 2019
- Valued at $1B
- Description: Data-powered marketing technology company that combines the third largest data set (2.4B+ identities) with results-driven AI to unlock consumer intent, personalize experiences, and drive customer acquisition, retention and growth
- PE-backed; Mezzanine financing ($90.79M) raised in December 2018
Product & Assortment-Focused Solutions
- Description: Predictive intelligence software that can convert IoT data into insights; Have product recommendation and store optimization solutions as well as solutions relevant to inventory management / supply chain
- Series A ($5M) raised in April 2019
- Description: AI platform that performs consumer engagement analytics, provides personal product recommendations and mobile optimized search to users by leveraging artificial intelligence, enabling businesses with actionable, timely and relevant insights
- Currently raising
B2C Players
- Description: Custom 3D printed footwear designed to promote body alignment and reduced foot fatigue
- Series A ($4M) raised February 2017
- Description: Provider of a global media and technology platform designed to offer personalized content and recommendations. The company’s platform leverages proprietary knowledge graph technology, which uses AI technology to customize and tailor content discovery across articles and videos on culture, travel and food recommendations, enabling users to discover the world and inspire them to explore, share and connect with others.
- Series B ($80M) raised April 2018
Risks
The main risks associated with collecting actionable shopper intelligence and increasing personalization through technology solutions are as follows:
- Customer trust and data security
- Regulation and privacy laws
Customer Trust and Data Security
While customers are demanding more high-touch, personalized experiences, they are also concerned about sharing too much data and their personal privacy. In fact, 86% of customers are concerned about their data privacy, and 79% of consumers believe companies know too much about them. [28] Customers can perceive personalization tactics as “creepy.” Therefore, it is important that retailers and technology providers address these concerns and proactively manage customer privacy.
Regulation and Privacy Laws
Regulatory risk has become increasingly important for retailers with the proliferation of digital channels and customer data collection. In fact, 46% of customers want increased governmental regulation to protect data privacy. [29] This is becoming a reality with acts such as the 2018 General Data Protection Regulation (GDPR) enforcement. As of January 2020, only 20% of businesses believe they are GDPR compliant. [30] Regulation, which may continue to evolve, makes it even more important that companies are able to manage their customer data properly. Solution providers that can provide meaningful solutions that are also compliant with privacy laws and regulation will be especially important.
Investment Landscape
Overall Funding Landscape
Funding for AI and machine learning in the retail and advertising/marketing space has grown significantly both in terms of value and number of deals over the past few years (refer to Figure 2 – note that this only goes through Q3 2019).
Figure 2
Recent notable deals include:
- ContentSquare: $60M Series C raised in January 2019
- Cardlytics, Inc.: $183.6M IPO in February 2018
In-store retail tech deals, with top themes in this space including autonomous stores and store analytics, increased in funding volume in 2019, but number of deals decreased, indicating a shift away from seed deals in this space toward Series A or later stage. Notable deals in these spaces include Trax, Skupos, RetailNext, Nepa Insights, Euclid (acquired by WeWork), Accel Robotics (RevTech portfolio company), among others. [31]
Funding for retail and CPG AI has accelerated (similar to retail and advertising AI [32] ):
Figure 3
Key themes include personalized marketing, messaging, and pricing solutions.
M&A Activity
In 2019, Nike acquired Celect, McDonald’s acquired Dynamic Yield, and Walmart acquired Aspectiva. All three of these acquisitions showcase major retail and hospitality players acquiring AI and personalization startups to help them scale personalized customer experiences. [33]
LetGo was acquired by eCommerce platform OfferUp in March 2020 for $120M.
Viable exit strategies for technology solutions in the Shopper Intelligence and Personalization space include selling to a customer (i.e. a retailer or hospitality provider, as in the case of Nike and McDonald’s), selling to a complimentary platform such as an eCommerce platform, a data lake, or other tech solutions, or selling to a large player in the space such as Adobe or IBM.
Conclusion
Shopper Intelligence and Personalization are increasingly important to retailers as they attempt to remain relevant to customer needs, lower CAC and reduce churn, and streamline merchandising and marketing processes. Effective solutions in the space have the potential to lead to billions of dollars in cost savings and revenue lifts in the retail industry. RevTech increasingly looks for end-to-end solutions that are easy for retailers to integrate with existing platforms. Furthermore, RevTech looks for disrupters that are fundamentally rethinking personalized customer offerings and experiences across all channels.
References
[1] Artificial Intelligence in Big Data Analytics and IoT: Market for Data Capture, Information and Decision Support Services 2017 – 2022, Mind Commerce Publishing, 2017
[2] Mind Commerce Publishing, 2017
[3] Mind Commerce Publishing, 2017
[4] Mind Commerce Publishing, 2017
[5] “Profiting From Personalization,” BCG, May 2017; https://www.bcg.com/publications/2017/retail-marketing-sales-profiting-personalization.aspx
[6] “Global Artificial Intelligence-based Personalization Market to Post 13% CAGR During 2018-2022| Technavio,” Business Wire, June 2018; https://www.businesswire.com/news/home/20180619005948/en/Global-Artificial-Intelligence-based-Personalization-Market-Post-13
[7] “Thriving in the New Normal,” Oliver Wyman, May 2020
[8] “The future of personalization—and how to get ready for it,” McKinsey, June 2019; https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/the-future-of-personalization-and-how-to-get-ready-for-it
[9] McKinsey, June 2019
[10] “Despite Recognizing the Importance of Personalization, Marketers Are Still Missing the Mark,” Jeff Hasen, AdWeek, October 2018; https://www.adweek.com/brand-marketing/despite-recognizing-the-importance-of-personalization-marketers-are-still-missing-the-mark/
[11] “26 Essential Personalization Stats for B2C Marketers,” SmarterHQ; https://smarterhq.com/blog/personalization-statistics-roundup
[12] “Why Marketers Struggle with Data-Driven Personalization,” Ross Benes, eMarketer, October 2018; https://www.emarketer.com/content/why-marketers-struggle-with-data-driven-personalization
[13] McKinsey, June 2019
[14] The Amazon Report, SmarterHQ, 2017
[15] “Customers Want Customization, and Companies Are Giving It to Them,” Aviva Freudmann, New York Times, March 2020; https://www.nytimes.com/2020/03/18/business/customization-personalized-products.html
[16] Business Wire, June 2018
[17] McKinsey, June 2019
[18] Marketers are on a Mission, SmarterHQ, June 2018
[19] BCG, May 2017
[20] “Bad Data Costs the U.S. $3 Trillion Per Year,” Thomas C. Redman, Harvard Business Review, September 2016; https://hbr.org/2016/09/bad-data-costs-the-u-s-3-trillion-per-year
[21] Harvard Business Review, September 2016
[22] “This is how big data analytics can increase profit in your company,” Carlota Feliu, Datumize; https://blog.datumize.com/how-big-data-analytics-can-increase-profit-company
[23] “Experience is everything: Here’s how to get it right,” David Clarke and Ron Kinghorn, PWC, 2018; https://www.pwc.com/us/en/advisory-services/publications/consumer-intelligence-series/pwc-consumer-intelligence-series-customer-experience.pdf#page=8
[24] “Transform Your Personalization Strategy at Forrester’s Consumer Marketing Forum,” Brendan Witcher, Forrester, March 2018; https://go.forrester.com/blogs/transform-your-personalization-strategy-at-forresters-consumer-marking-forum/
[25] “These 5 Brands Aced Personalized Marketing in 2018,” Sofie Lundberg, Global Web Index, January 2019; https://blog.globalwebindex.com/marketing/personalized-marketing-2018/
[26] Mind Commerce Publishing, 2017
[27] Funding stage details and descriptions based on Pitchbook
[28] “Consumers Trust Amazon Is Using Their Data Responsibly, Beating Out Apple, Google & Banks, According to New SmarterHQ Survey,” Scott Cianciulli, Business Wire, March 2019; https://www.businesswire.com/news/home/20190304005148/en/Consumers-Trust-Amazon-Data-Responsibly-Beating-Apple
[29] McKinsey, June 2019
[30] “What is GDPR and how does it impact your business?” Jennifer Lund, SuperOffice, January 2020; https://www.superoffice.com/blog/gdpr/
[31] Tracxn
[32] Tracxn
[33] “AI Startups Are Helping Big Brands Offer Personalization At Scale,” CBInsights, August 2019; https://www.cbinsights.com/research/mass-production-mass-personalization/