Is Customer Decisioning Having Its "CDP Moment"?
Why Customer Decisioning Could Be the Hottest Space in MarTech Battleground?
I’ve been a passionate practitioner of Customer Decisioning for over a decade, and I can’t help but notice how much this space has evolved, especially in the last couple of years. I believe Customer Decisioning is having its 'CDP moment.'
Let’s dive in.
The CDP Boom: A Quick Look Back
The CDP category grew rapidly around 2016–2020 as marketers embraced unified customer data. By mid-2024, the CDP Institute was tracking 194 CDP vendors worldwide, employing over 16,800 staff and backed by $7.5 billion in funding. This represents a dramatic rise from just a handful of CDPs in 2016, illustrating how a marketing technology concept can go mainstream in a short span. CDP industry revenue reached about $2.4 billion in 2024, and even enterprise giants like Salesforce and Adobe eventually launched or acquired their own CDP offerings to meet demand. In short, the CDP’s “moment” was characterised by rapid vendor multiplication, significant venture funding, and broad recognition of CDP as an essential MarTech component.
Are we seeing a similar pattern with the Customer Decisioning technologies?
One hallmark of the CDP wave was the proliferation of new startups and solutions entering the space. We now see a comparable uptick in Customer Decisioning platforms, particularly those leveraging AI and reinforcement learning to automate 1:1 decisions. Here are a few notable examples:
Hightouch – Originally known for “reverse ETL” data integration, Hightouch expanded into AI-driven decisioning in 2024. The company released a new AI Decisioning product that uses AI agents to decide the best message or experience for each customer. Investor enthusiasm has followed: in early 2025, Hightouch raised an $80 million Series C (at a $1.2 billion valuation) to fuel this AI personalisation vision.
OfferFit – OfferFit founded in 2020 boldly proclaimed “A/B testing is dead.” Their platform uses reinforcement learning to continually experiment and find the optimal offer, channel, and timing for each customer. The approach has attracted major funding and customers. In late 2023, OfferFit secured a $25 million Series B. In March 2025, Braze announced its acquisition of OfferFit for $325 million.
Aampe – Aampe is another startup exemplifying the trend. It provides “agentic” AI infrastructure to personalise mobile app experiences in real time. In December 2024, Aampe announced it had deployed over 100 million AI agents across user devices worldwide, handling on the order of 15–200 billion decision events per week for consumer apps. The company raised $18 million in Series A funding to accelerate the adoption of its AI-agent approach.
Movable Ink (Da Vinci) – Movable Ink, known for content personalisation, has been talking up AI decisioning in marketing. Their 2025 trends predict that marketers will “take back your day-to-day with AI decisioning and agents” to deliver exponential lift, making AI-driven decisioning a centrepiece of marketing strategy by 2025.
These examples illustrate a flurry of new entrants and products in the decisioning space, many of them securing significant funding and rapidly scaling. Such growth in the vendor ecosystem mirrors the early explosion of CDP providers.
Big Boys Arrive
Another sign of a “moment” is when big industry players validate the space via major product moves or acquisitions. We’re seeing this in the Customer Decisioning space.
Braze acquires OfferFit (2025) – Braze’s CEO noted this will give marketers “a full spectrum of machine learning capabilities” and elevate them “from the drudgework of campaign creation” to orchestrating optimised customer journeys with AI.
Adobe and Salesforce – The enterprise marketing clouds are also deeply embracing real-time decisioning.
Adobe’s Experience Platform added a Real-Time Offer Decisioning engine that uses a centralised catalogue of offers and machine learning to determine the best offer for each user in the moment. Adobe Journey Optimizer, launched in 2021, is explicitly focused on decision management, allowing marketers to set up AI-driven rules and constraints so each customer gets the right next-best experience across channels.
Salesforce has integrated decisioning into its Marketing Cloud: for example, Marketing Cloud Personalization (built on the Evergage acquisition) delivers 1:1 content recommendations in real time, and Salesforce offers an Einstein Next Best Action tool to guide offers in sales and service interactions. I won’t be surprised if Salesforce Data Cloud starts providing more and more emphasis on Decisioning.
These tech giants, recognising the need for an “intelligence layer” between data and engagement (often termed a decisioning hub) reinforces that customer decisioning is now a core capability, not a fringe idea.
Pega Customer Decision Hub – Pegasystems has been a pioneer here, offering real-time AI decisioning in its Customer Decision Hub for years. Pega’s approach (treating each customer as a “segment of one” with next-best-action recommendations) is being echoed and adopted widely across the industry. Other established players like SAS and IBM have also doubled down on decision automation.
One notable group that is increasingly investing in this space without calling this out as “Customer Decisioning” is hyperscalers, especially DataBricks and Snowflake. Databricks announced Data Intelligence for Marketing in 2024. Snowflake Crotex AI is perfectly positioned for true customer decisioning on the great dataset that it controls.
Databricks Summit 2025 : Databricks is no longer just a lakehouse. It aims to be an end-to-end decisioning platform—one who knows the meaning behind the data.
Market leaders are investing in or acquiring decisioning capabilities, validating that this technology is critical for modern marketing and customer management. This mirrors how, at the height of CDP hype, big firms - Salesforce, Adobe scrambled to add CDPs to their portfolios. Salesforce has specifically made multiple attempts in CDP - starting with Customer 360 audiences to Salesforce CDP, Marketing Cloud Customer Data Platform, Salesforce Genie and currently Salesforce Data Cloud.
The Future of Customer Decisioning is Bright
Gartner predicts that by 2025, around 30% of all outbound marketing messages from large enterprises will be generated by AI – a scenario that practically requires robust AI decision engines to decide which message to send to whom and when.
Marketers are gearing up for an AI-driven future: the AI marketing technology market is projected to exceed $100 billion by 2028, and a significant portion of that will be tools that analyse customer data and automate decision-making processes for personalisation and targeting.
In other words, the ingredients fueling the customer decisioning trend (cheap computing power, abundant data, advanced ML algorithms, and pressure for better personalisation) are only getting stronger.
Who’s who in Customer Decisioing Space
This list is in no way exhaustive, but based on my reading and following. If you are a player who operates in Customer Decisioning Space, please drop me a message at contact@martechsquare.com and I will have a look.
Pegasystems – Pega’s Customer Decision Hub is a longtime leader in 1:1 decisioning for customer engagement. It uses always-on AI models to recommend the “next best experience” in marketing, sales, service or retention contexts.
Adobe – Offers Adobe Journey Optimizer and Real-Time Decisioning services within Adobe Experience Cloud, allowing marketers to configure offer decision rules and apply AI to choose the best offer/content in real time. Adobe’s solution is tightly integrated with its CDP.
Salesforce – Provides Marketing Cloud Personalization (from the Evergage acquisition) for web/app personalisation and Einstein AI features like Next Best Action across its clouds. Salesforce’s vision is that AI-driven decisioning is embedded in journey orchestration and CRM interactions to optimise every customer touch.
Braze – Now with OfferFit under its wing, Braze delivers a reinforcement learning decisioning engine within its Customer Engagement Platform. This combination enables Braze clients to automate experiment-driven personalisation on top of Braze’s real-time messaging and journey execution.
SAS – Offers SAS Intelligent Decisioning and related analytics tools. SAS is a strong player in combining data, analytics, and rules to automate decisions in domains from marketing to fraud prevention.
IBM – Provides IBM Watson Decision Platform capabilities (integrated in tools like IBM Watson Assistant and IBM Cloud Pak for Business Automation) enabling businesses to apply AI logic for customer interactions.
Hightouch – Hightouch positions itself as a Composable CDP + AI Decisioning platform. Hightouch uses a warehouse-centric approach: it pulls unified data (like a CDP) and then applies AI agents and reinforcement learning to optimise marketing actions for each user.
Aampe – Innovator in in-app personalisation, deploying lightweight AI “agents” to each user (especially in mobile apps) which then decide how the app’s content or offers should adapt for that user.
Movable Ink – Movable Ink’s platform can act as an AI decision engine for creative content, determining which image or copy variation to show each customer at email open time or on a webpage.
Others – Many other tools contribute to this ecosystem: Dynamic Yield (now owned by Mastercard) offers AI-driven web personalization and testing; Zeta Global and Iterable include AI decision features in cross-channel campaigns; Oracle CX has real-time intelligent decision capabilities (stemming from its Oracle RTD product); and open-source or niche solutions for decision orchestration are emerging as well. Even rule-based decisioning engines (like FICO’s Blaze Advisor or various customer journey orchestration tools) are evolving by adding machine learning to become smarter over time. This all feeds into the broader trend of smarter, faster decision-making in customer management.
As this list shows, customer decisioning is a broad and growing arena. Both legacy enterprise vendors and nimble startups are vying to provide “the brain” that sits between customer data (often from a CDP or data warehouse) and customer touchpoints (apps, websites, call centres, marketing channels).
This position in the tech stack is increasingly prized.
I am convinced that Customer decisioning is having its “CDP moment.” The parallels are striking: a few years ago, companies realised they needed a centralised customer data platform (CDP) and rapidly adopted those systems; today, they are realising that having data isn’t enough - we need an intelligent decision engine to use that data in real time for each customer. The surge in interest, innovation, and investment around customer decisioning over the past year is similar to that of the CDP frenzy circa 2018–2019. We see it in the numbers (funding rounds, new product launches, vendor growth) and the validation by major industry players and analysts.
The concept of a “Decisioning Brain” – an always-on AI that determines the best interaction for each customer – has moved from an experimental idea to a must-have capability. With nearly every marketing cloud and a crop of well-funded startups racing to offer these decision hubs, 1:1 customer decisioning is fast becoming mainstream. In the words of an industry expert, “this isn’t optional; it’s the difference between sounding irrelevant and sounding like you understand your customer”.
P.S. I’ll be watching this space (obsessively).