AI Marketing Landscape 2019Tuukka Valkeasuo, Tom Nickels, Ola Ottosson, Katariina Lahdenpää

In our series of marketing technology landscapes, this time Avaus’ team has focused on mapping AI solutions. This, first ever published AI Marketing Landscape tells a compelling story about the new urgency of data and analytics in all areas of business, enterprise, SME and smaller. As we speak, the Landscape is evolving. Adoption of AI tools and technologies is gaining speed, and the Landscape is as of this writing, for sure already wanting. The AI based solutions are proliferating on the SAAS-market as ever more new AI-pureplay entrants compete with AI-upgraded older incumbents. Most of the buzz can be attributed to real advances in analytics tools development, but simultaneously, the AI frenzy should partly be attributed to current branding concerns. Everyone in town today needs to wear the AI inside - tag.

Experience optimisation and personalisationGeneral marketing and customer engagement, personalisation, digital advertising, sales and customer care management and automation. These solutions are implemented on top of existing data platforms, existing customer databases, DMP’s or Google Analytics.

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Process efficiency and automationMarketing processes and decisioning tools (Sales, Marketing, Customer Care), Tools forsales, content development, management, decisioning support, automation algorithms ofmartech stacks.

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Data ManagementTools for managing data assets and – data capture, voice recognition, social listening, Data management, ML, frameworks tools libraries, transparency and compliance.

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The biggest domain within the AI-landscape is marketing and customer engagement solutions Within this domain we found three different subdomains:

According to market research firm Tractica the AI market are expected to grow eightfold, from an estimated present $15BN to close to $120BN in 2025. This represents a compound annual growth of 40+%. The broader market crosses all industries and this Landscape represents only a fraction of the total, focusing on technologies mainly of relevance for customer engagement and marketing.

Natural language processing is developing rapidly and is required by several apps such as chatbots, digital agents, content creation, and data extraction. Programmatic advertising with an AI twist is already becoming old news, but US firms AppNexus and Mediamath, with a joint revenue of $800M are on first and third place on the list of the best funded companies of the Landscape.

Process efficiency and automation will reduce costs and accelerate more intelligent ways of working. According to an Accenture and Frontier Economics study (2019) the impact of AI on labor productivity in developed countries in 2035 is looking astonishing, from a Nordic perspective. Sweden is scoring first (37%) and Finland second (36%) with U.S. in third place (35%).

Personalisation is the main theme throughout, whether it is in advertising, web site, web store or mobile app. Good examples are Swedish Findify, Finnish, Leiki and Nosto (that has raised a total of $32,8M in funding) that all three belong to the personalization category. The Swedish company Artificial Solutions is in the sub-category of interaction and digital agents.

Nordic AI Companies in 2019

Process automation and efficiency will eliminate time consuming repetitive tasks for sales reps such as scheduling and prospecting. Good examples are US firms Insidesales.com ($251M funding) and Seismic ($164,5M), both focusing on sales automation. The first generation chatbots have received a lot of flak for their undeniably low IQ in many situations, but they will continue to make further customer care inroads with each new generation. They still provide a big potential for CX improvement, especially for SME:s lacking CC staffing resources. A Swedish star seems to be Artificial Solutions with $34,5M in funding and $8M in revenues. Many of the solutions in the Landscape pre-date the AI-era. Some of the firms have simply added AI-features and functionalities in their latest releases. An example of this is Adobe Experience Cloud and Salesforce Einstein. In a few cases AI is pure cosmetics and plays a much smaller role than is claimed in the sales pitches.

Big or Small Brain? The two main trends in enterprise AI-based analytics solutions are build or buy. The Landscape consists primarily of the latter - special purpose solutions ready to run on the go. We call them Small Brain solutions. They are designed to do a job with a narrow scope or purpose and they do not require big development efforts to implement. General purpose AI, we like to call Big Brain solutions. They reside primarily in the Data Management part of the Landscape and they represent framework technologies used for custom built enterprise platforms such as the algorithms of a big e-tailers like Zalando.

The AI-Landscape is also well populated with different kinds of citizen data science solutions. They provide tools for the non-coding communities of analysts. Good examples are natural language query applications that make data more accessible to anyone with a question. The Swedish firm Peltarion (funding $36,8M) aims at bringing machine learning closer to business management. Peltarion has created an operational AI platform, easy to understand and run and that should lower the threshold AI technology adoption for businesses, large or small. Avaus has published a partnership with Peltarion in early 2019. These data citizen solutions are gaining ground as Data Scientist tend to come from increasingly diverse backgrounds, other than computer sciences. Many have degrees in macro- and micro economics, physics or other natural sciences.

CMS-generated websites replaced a long time ago code based custom coded solutions. In Data Science this translates into today’s applications that enable sophisticated algorithm design without actually working with Python or other programming languages of choice in the Data Scientist community.

Off-the shelf algos for sale

The Landscape also reveals a process of algorithm commoditisation. The ability to create a competitive advantage with pure algorithmics is becoming harder, as it will get increasingly easier to use prefab-algolibraries. The advantage is accordingly shifting towards proprietary data, as data cannot be copied and commoditised. The data advantage works on both macro and micro levels. The platform companies are examples of how huge data volumes beats everything in sight. This applies on company levels as well. Players with better and more dense data, will win beat the competition in the next years to come. A good example of commoditization is RELEX Solutions, the Finnish superfunded ($223.9M) AI-driven retail planning solution with algorithm libraries for almost any aspect of retailing – ranging from demand forecasting to workforce optimization.

Transparency and compliance will play a more dominant role within the AI ecosystems in the future. The Finnish company Saidot is a good example of new solutions aimed at enabling creating trust and transparency of algorithms in an AI-driven world. Avaus has developed its data science offering since 2016. Today Avaus analytics related revenue is about to eclipse revenues from martech implementation and development work. This reflects an overall shift in the market. The digitalisation has reached a point where most B2C and many B2B companies have implemented some basic engagement technologies and are now looking at how to ensure yields of their investments. This has turned the focus on data and analytics.