beyond data
    Cross-Sell & Next-Best-Action Recommendations

    One recommended action per customer, grounded in their own data, delivered where your reps already work.

    Cross-Sell & Next-Best-Action Recommendations before and after
    Outcomes

    What changes once this is in

    5-15% lift in cross-sell revenue per contact

    Measured against the pre-launch baseline on the same rep cohort, typically visible within the first quarter of full rollout.

    New starters reach tenure-level performance faster

    Agents stop guessing. A six-week-old rep with a good recommendation outperforms a tenured rep relying on memory and product cheat sheets.

    Offer fatigue falls

    Customers receive one relevant recommendation at the moment of contact, not five generic ones in the monthly email. Opt-out rates on marketing comms fall alongside.

    The system compounds

    Every accept and decline is training data. The model gets sharper every week it runs. This is the feedback loop that separates an Insight-to-Action System from a one-shot pilot.

    A recommendation engine that tells reps and agents the single best product, service, or offer for each customer right now, grounded in that customer's own transaction history. It embeds in the CRM or service desk, surfaces one action with a one-line reason, and learns from every accept, decline, or snooze. Designed for growing businesses with a CRM, a billing or policy system, and agents who want something better than head-office scripts.

    The Problem

    What usually breaks

    The big-four version of this costs $8M over two years. A data science team of 30, a Martech stack that takes a quarter to reconfigure, and a real-time decisioning platform with a licence fee north of seven figures. That enterprise version is not what most relationship-led operators need. A focused next-best-action system is smaller, cheaper, and lands faster, because it works with fewer channels and the CRM your reps already use.

    What the buyer has today is usually one of two things. Either head office sends out a monthly campaign that treats every customer like the same customer, and reps ignore it. Or the CRM shows the rep a profile page with 50 fields, and the rep clicks through maybe four of them before the call ends. Both are guessing, just at different volumes.

    The gap in the middle is where growing banking, insurance, telco, distribution, and aftersales businesses lose cross-sell revenue every week. One relevant recommendation, delivered at the moment of contact, closes that gap.

    In Your Business

    How this lands inside your operation

    The recommendation appears inside Salesforce on the account record itself, one banner at the top. When a relationship manager at your credit union pulls up a member for their quarterly call, the CRM shows a single prompt: "Flag for home-loan top-up. Offset balance up 40% over six months, home-loan rate 60 bps above market." Three buttons: accept, decline, snooze. That is it. No extra tab, no separate portal, no weekly PDF.

    Behind the banner sits a model trained on your own transaction, policy, and service history, scoring every customer every night and picking the one action most likely to land. Every accept, decline, and snooze feeds back into tomorrow's score. The model learns which recommendations your reps actually trust. That feedback loop is the difference between a model that compounds and one that decays.

    This is for you if…

    • Head of retail banking at a credit union or mutual, running relationship managers who know their members but are time-poor on calls
    • Head of customer operations at a insurer, running contact-centre agents who handle renewals, claims, and enquiries
    • Head of sales at a B2B distributor, running inside-sales reps who take repeat orders and need to grow basket value
    • Any business with a CRM, a billing or policy system, and agents receiving offers from head office that feel generic

    This probably isn't the right fit if…

    • Businesses without a CRM or system of record where the recommendation can actually land in front of a human
    • Product sets under ten SKUs where there is no real choice to make, a rules engine is cheaper
    • Teams without the mandate to act on the recommendations, the model is worthless if the answer is always snooze
    By industry

    Sample recommendations, by industry

    One row per sector. The recommendation is the single action a rep or agent sees on screen. The reason is the one line that sits under it.

    IndustryExample next-best actionReason line the rep sees
    Credit unionFlag for home-loan top-upOffset up 40% over 6 months, rate 60 bps above market
    Retail bankOffer offset-linked savings sweep$18k sitting in transaction account above 90-day average
    General insuranceReview sum-insured for CPI upliftBuilding sum insured unchanged 4 years, CPI +19%
    Health fundSuggest extras upgrade pre-renewalTwo dental claims declined this year against base cover
    TelcoMove to family planSecond active SIM on same billing address, out of contract
    B2B distributionAdd high-margin complement to next orderCore line reordered weekly, complement attached on 62% of similar accounts
    Automotive aftersalesBook brake-pad service at next log-book visitPad wear indicator at 3.2mm, next service due in 900km
    Membership businessOffer loyalty tier upgradeThree cross-category purchases in 60 days, spend above tier threshold
    Today vs with NBA

    The split hero: what the rep sees

    The split hero: what the rep sees

    Two frames side-by-side.

    Left frame, labelled "Today". A standard CRM customer record. 50 fields visible. Name, date of birth, address, phone, email, products held (six lines), balances, renewal dates, recent interactions, notes, segment codes, marketing flags, contact preferences, household link, advisor assignment. A scroll bar on the right. The rep's eye is somewhere in the middle. Nothing is highlighted.

    Right frame, labelled "With next-best-action". The same CRM, same header, but one banner has moved to the top of the screen. Inside the banner: the action ("Flag for home-loan top-up"), one line of reason ("Offset balance up 40% over six months, home-loan rate 60 bps above market"), and three buttons: Accept, Decline, Snooze. Below the banner, the customer record is still there for anyone who wants to scroll. Most won't need to.

    Caption under both frames: "Same data. Same CRM. One decision the rep can actually make in the 30 seconds before the call connects."

    How It Works

    The engagement, step by step

    1. 1

      Map your customer data: transactions, policies, products held, service interactions, and the actions reps currently log.

    2. 2

      Stand up the AgileData platform if needed, or connect to your existing platform, so the model has one clean view of each customer.

    3. 3

      Train an initial recommendation model on 12-24 months of history, tuned to your product catalogue and margin profile.

    4. 4

      Embed the recommendation as a single banner in the CRM, service desk, or call-centre screen reps already use.

    5. 5

      Launch to a pilot team with the three-button interaction: accept, decline, snooze, each with a one-line reason captured.

    6. 6

      Close the loop weekly: accepted actions update the model, declines and reasons retrain the ranker, snoozes recycle.

    7. 7

      Expand to the full field once accept rates and revenue lift hold up against the baseline agreed before launch.

    Capabilities

    The capabilities behind this

    Advanced Analytics & ML

    Predictive models, recommendation systems, forecasting, and MLOps. Every model is measured against the decision it was built to serve.

    Learn more

    AI Development

    AI agents, chat interfaces, and multi-agent systems, built end to end by the engineer who scoped them. Deployed, monitored, and handed over.

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    AgileData Platform

    One governed data platform across your ERP, CRM, finance, and operational systems. We build it, we run it, and it is live in two weeks.

    Learn more
    Common Questions

    Frequently asked

    Let's talk about Cross-Sell & Next-Best-Action Recommendations.

    30-minute call, no slides, no obligation. We'll tell you plainly whether this is the right fit for what you're trying to do.

    See related: Sales Prioritisation Engine

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