Adobe has launched its Intelligent Services powered by the AI capabilities of Adobe Sensei. Built on Adobe Experience Platform, the new services will help brands overcome common challenges associated with AI. This may include a lack AI expertise and implementation complexities.
The new Intelligent Services will be able to stitch together unstructured data under a common language. Strict governance features available help brands more easily stay ready for industry regulation and corporate policies for consumer safeguards. Adobe says the self-service interface delivers flexibility. This allows users to configure the services for use cases specific to Customer Experience Management (CXM).
Leveraging Intelligent Services
Visual computing company NVIDIA has been one of the first to leverage Intelligent Services for their marketing efforts. Its team used the Attribution AI service to understand the effectiveness of marketing programmes. These additional insights drove 5 times more registrations to event campaigns. NVIDIA also used the predictive insights in Customer AI to better understand how consumers were engaging with its gaming products. The purpose, to drive more personalised content for users. Lastly, their team used Journey AI to predict optimal send times for emails and engage customers according to their preferences. This test drove a 14% lift in email open rates and validated that predictive insights can help enhance email effectiveness.
“There’s no denying that AI is already empowering brands to deliver more relevant experiences. However, it still hasn’t reached full potential within most organisations,” said Steve Allison, Head of Product Marketing, Audience and Platform Technologies, EMEA.
The full set of Intelligent Services (two in GA, the rest in Beta) on Adobe Experience Platform will include:
Brands often do not have resources to dig deep into their data and understand the underlying reasons behind customer actions. Customer AI helps them analyse historical and real-time data across the business to address this. The platform creates propensity scores for key events like churn or conversion. A subscription service, for example, could receive a segment of users likely to unsubscribe due to price sensitivity. At this point, the brand could engage with a custom promotion.
Marketers have multiple touchpoints with customers (e.g. web, email or social) that require resource and time investment. Attribution AI empowers teams to quantify the incremental impact of each touchpoint. It uses an advanced approach to measure true marketing effectiveness and inform budgets. It is unique from rules-based methods, where often too much credit is given to “first-touch” (e.g. web visit). Or “last-touch” (purchase event), leading to artificial rules that could skew decision-making.
Journey AI (Beta).
Even loyal customers have a patience threshold when it comes to marketing. With more channels than ever, knowing when to engage and managing fatigue has become a bigger focus. Journey AI can support brands predict the best time, frequency and channel to market for customers. This includes a fatigue score, which can be used to gauge engagement for consumers. A retailer, for example, can use this ahead of the holiday shopping season to manage promotions.
Content & Commerce AI (in Beta).
Brands have embraced the idea that creative also needs to perform well. Content and Commerce AI delivers guidance on variables that result in high performance, such as colours or subjects. It also takes on the task of automatically tagging assets, for better searchability in the production stages. On the eCommerce side, the AI will automate product recommendations based on real-time signals and customer preferences.
Leads AI (Beta).
B2B marketers have unique challenges when it comes to engaging prospects and existing customers. Long sales cycles make it difficult to see the impact of ongoing marketing and where prospects are in the purchase journey. Leads AI uses real-time behavioural signals to help brands predict leads that are likely to turn into tangible opportunities. It can enable an enterprise software vendor, for instance, to drive targeted campaigns with better personalisation.
Adobe has been leveraging the Intelligent Services internally. It powers Adobe’s data-driven operating model (“DDOM”). A framework that drove its transformation from box software to the cloud. Over 1.5 billion propensity scores are produced daily, showing how likely customers are to take a particular action (e.g. unsubscribe). Or generate target audiences that have been up to 5 times more valuable.
Enterprise Times: What this means for business?
Adobe appears to be completely embracing AI. Last year it announced a series of innovations to enhance its Adobe Experience Platform at the MagentoLive event in Amsterdam. The company had earlier in the year unveiled Adobe Experience Platform Data Science Workspace. The platform aims to empower everyone, regardless of their level of technicality, with AI-powered real-time intelligence. Adobe suggests brands can use Adobe Sensei, its AI and machine learning technology, to better personalise and understand their audience.
According to IDC, spending in the areas of AI are expected to increase, even when budgets contract. Organisations are looking for new ways to drive growth while also maintaining costs. Analysts believe AI solutions have the ability to support both. When properly implemented, AI can uncover insights in data to help refine customer experiences or optimise marketing investments. However, as brands begin to embrace AI in earnest, many will have to contend with challenges that have long plagued its adoption. The lack of AI expertise and implementation complexities.