The rep opens the day knowing which customer to see and why. That's next best action and next best customer done right — prioritized, explainable, and measured in calls made and scripts moved.
Built for commercial teams at Telix, Azurity, Alkermes, and UCB.
Next best action (NBA) tells a rep the highest-value move for each customer; next best customer (NBC) tells them who to prioritize first. Done right, it isn't a black box — it's a ranked, explainable list in the CRM reps already use, judged by whether the field adopts it and the scripts move.
A model can rank every HCP perfectly and still change nothing. If the recommendation shows up late, unexplained, or outside the rep's workflow, it gets ignored — and the investment dies quietly in the field.
The model can be right.
The list can be ranked.
If the rep doesn't trust it, none of it reaches a patient.
The highest-value move for a given customer, right now — call, send, or wait.
Who to prioritize first, so limited field time lands where it matters.
Delivered inside Veeva or Salesforce — not a separate portal nobody opens.
Every recommendation carries its why: the signal that drove it. Reps act on what they understand, so we show the logic, not just the rank — and the field adopts it.
Adoption is the metric that matters. We track it, and tune the model to how reps actually work.
The recommendation isn't the point; the call that gets made is. So we run the whole distance — signal to the rep's screen to the script — and keep the technology backstage.
See the signals, decide the priority, act in the CRM, and Track adoption and scripts so the model earns its place.
You get recommendations reps understand and use, and a firm that knows exactly where the models help and where judgment has to. The algorithm is the enabler; the call made is the point.
Two questions, one system — tuned to the way your field team really plans a week.
NBC narrows the universe to the customers worth your limited field time; NBA tells the rep the best move for each. One without the other either wastes calls on the wrong targets or shows up to the right ones with nothing to say.
Recommendations land in Veeva or Salesforce with the reason attached — no separate login, no retraining. If it isn't in the flow of the rep's day, it doesn't get used, and we build for use.
Every row ties to a move a rep or manager actually makes — and how we get there.
| Decision | The question in the field | What you get | How we get there |
|---|---|---|---|
| Who first | Which customers deserve my limited time? | A ranked next-best-customer list | NBC models on Rx, claims & potential |
| What move | What's the best action for this HCP? | Next best action with the reason shown | NBA models in your CRM |
| Why this | Can I trust the recommendation? | Explainable signals behind each call | Transparent model logic |
| Did it work | Is the field adopting it? | Adoption & script-impact tracking | Closed-loop feedback in Dataiku |
Bundled, this becomes part of PharmaIQ — our commercial intelligence layer for specialty pharma.
An AI/ML segmentation and next-best-action framework for a $700M portfolio generated a $45M revenue opportunity and identified $120M more in commercial potential.
Read the case study →“The gap between data science and commercial execution isn't a technology problem — it's a thinking problem. Nasheed and his team bring the clarity that helps leaders ask better questions and build systems that support how decisions actually get made.”
The questions we hear most from commercial, field-ops, and analytics leaders.
Next best action recommends the single highest-value move for each customer at a given moment — which HCP to call, what to lead with, or when to hold. In pharma it blends prescribing, claims, payer, and engagement signals into a ranked, explainable recommendation delivered in the rep's CRM.
NBC answers who to prioritize — the customers worth a rep's limited time. NBA answers what to do with each one. You need both: NBC without NBA sends reps to the right doors with nothing to say; NBA without NBC spreads effort across the wrong targets.
Both can work. If you already own a platform, we make it perform — better signals, cleaner targeting, real adoption. If you don't, we build next best action on the stack you have, as a Dataiku software partner, without committing you to a new seat-based tool. The right answer depends on your data and field model, and we'll tell you plainly.
In the flow of their day — inside Veeva or Salesforce, with the reason attached — not in a separate portal. Adoption lives or dies on whether the recommendation shows up where reps already work, so that's where we put it.
Because it's explainable and we measure adoption. Every recommendation shows the signal behind it, and we track whether the field acts on it — then tune the model to how reps actually plan, rather than asking them to change for the model.
Typically prescribing (NRx/TRx/NBRx), claims, payer and access, and engagement history. We work from the data you already license and connect it, rather than forcing a new vendor.
One conversation. We'll tell you what it would take to get next best action your reps actually adopt.
We'll tell you what it would take to put next best action in your reps' hands.
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