www.causalens.com Open in urlscan Pro
192.124.249.67  Public Scan

Submitted URL: https://t.causalens.com/t/106328/c/4184445b-04df-4fd7-85ca-7cd4d893482b/NB2HI4DTHIXS653XO4XGGYLVONQWYZLOOMXGG33NF53WQ2LU...
Effective URL: https://www.causalens.com/white-paper/next-best-action-with-causal-ai/?utm_source=social-organic-linkedin&utm_medium=psa&u...
Submission: On June 16 via manual from US — Scanned from DE

Form analysis 3 forms found in the DOM

GET https://www.causalens.com/

<form role="search" method="get" class="search-form" action="https://www.causalens.com/">
  <label>
    <span class="screen-reader-text">Search for:</span>
    <input type="search" class="search-field" placeholder="Search …" value="" name="s">
  </label>
  <input type="submit" class="search-submit" value="Search">
</form>

POST https://forms.hsforms.com/submissions/v3/public/submit/formsnext/multipart/3400903/4cf43c92-a19a-4f10-b08e-b4a3ea334b24

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    <div class="hs_firstname hs-firstname hs-fieldtype-text field hs-form-field" data-reactid=".hbspt-forms-0.1:$0.1:$firstname"><label id="label-firstname-4cf43c92-a19a-4f10-b08e-b4a3ea334b24" class="" placeholder="Enter your First name"
        for="firstname-4cf43c92-a19a-4f10-b08e-b4a3ea334b24" data-reactid=".hbspt-forms-0.1:$0.1:$firstname.0"><span data-reactid=".hbspt-forms-0.1:$0.1:$firstname.0.0">First name</span><span class="hs-form-required"
          data-reactid=".hbspt-forms-0.1:$0.1:$firstname.0.1">*</span></label>
      <legend class="hs-field-desc" style="display:none;" data-reactid=".hbspt-forms-0.1:$0.1:$firstname.1"></legend>
      <div class="input" data-reactid=".hbspt-forms-0.1:$0.1:$firstname.$firstname"><input id="firstname-4cf43c92-a19a-4f10-b08e-b4a3ea334b24" class="hs-input" type="text" name="firstname" required="" value="" placeholder="" autocomplete="given-name"
          data-reactid=".hbspt-forms-0.1:$0.1:$firstname.$firstname.0" inputmode="text"></div>
    </div>
    <div class="hs_lastname hs-lastname hs-fieldtype-text field hs-form-field" data-reactid=".hbspt-forms-0.1:$0.1:$lastname"><label id="label-lastname-4cf43c92-a19a-4f10-b08e-b4a3ea334b24" class="" placeholder="Enter your Last name"
        for="lastname-4cf43c92-a19a-4f10-b08e-b4a3ea334b24" data-reactid=".hbspt-forms-0.1:$0.1:$lastname.0"><span data-reactid=".hbspt-forms-0.1:$0.1:$lastname.0.0">Last name</span><span class="hs-form-required"
          data-reactid=".hbspt-forms-0.1:$0.1:$lastname.0.1">*</span></label>
      <legend class="hs-field-desc" style="display:none;" data-reactid=".hbspt-forms-0.1:$0.1:$lastname.1"></legend>
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      <legend class="hs-field-desc" style="display:none;" data-reactid=".hbspt-forms-0.1:$1.1:$jobtitle.1"></legend>
      <div class="input" data-reactid=".hbspt-forms-0.1:$1.1:$jobtitle.$jobtitle"><input id="jobtitle-4cf43c92-a19a-4f10-b08e-b4a3ea334b24" class="hs-input" type="text" name="jobtitle" required="" value="" placeholder=""
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      <legend class="hs-field-desc" style="display:none;" data-reactid=".hbspt-forms-0.1:$1.1:$email.1"></legend>
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      <legend class="hs-field-desc" style="display:none;" data-reactid=".hbspt-forms-0.1:$3.1:$company.1"></legend>
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      <div class="input" data-reactid=".hbspt-forms-0.1:$5.1:$utm_source.$utm_source"><input name="utm_source" class="hs-input" type="hidden" value="social-organic-linkedin" data-reactid=".hbspt-forms-0.1:$5.1:$utm_source.$utm_source.0"></div>
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      <div class="input" data-reactid=".hbspt-forms-0.1:$6.1:$utm_term.$utm_term"><input name="utm_term" class="hs-input" type="hidden" value="" data-reactid=".hbspt-forms-0.1:$6.1:$utm_term.$utm_term.0"></div>
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  <fieldset class="form-columns-1" data-reactid=".hbspt-forms-0.1:$7">
    <div class="hs_utm_medium hs-utm_medium hs-fieldtype-text field hs-form-field" style="display:none;" data-reactid=".hbspt-forms-0.1:$7.1:$utm_medium"><label id="label-utm_medium-4cf43c92-a19a-4f10-b08e-b4a3ea334b24" class=""
        placeholder="Enter your UTM Medium" for="utm_medium-4cf43c92-a19a-4f10-b08e-b4a3ea334b24" data-reactid=".hbspt-forms-0.1:$7.1:$utm_medium.0"><span data-reactid=".hbspt-forms-0.1:$7.1:$utm_medium.0.0">UTM Medium</span></label>
      <legend class="hs-field-desc" style="display:none;" data-reactid=".hbspt-forms-0.1:$7.1:$utm_medium.1"></legend>
      <div class="input" data-reactid=".hbspt-forms-0.1:$7.1:$utm_medium.$utm_medium"><input name="utm_medium" class="hs-input" type="hidden" value="psa" data-reactid=".hbspt-forms-0.1:$7.1:$utm_medium.$utm_medium.0"></div>
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  </fieldset>
  <fieldset class="form-columns-1" data-reactid=".hbspt-forms-0.2">
    <div class="legal-consent-container" data-reactid=".hbspt-forms-0.2.0">
      <div class="hs-richtext" data-reactid=".hbspt-forms-0.2.0.2">
        <p>causaLens needs the contact information you provide to us to contact you about our products and services. You may unsubscribe from these communications at any time. For information on how to unsubscribe, as well as our privacy practices
          and commitment to protecting your privacy, please review our <a href="https://www.causalens.com/privacy-policy/" rel="noopener" target="_blank">Privacy Policy</a>.</p>
      </div>
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  <div class="hs_submit hs-submit" data-reactid=".hbspt-forms-0.5">
    <div class="hs-field-desc" style="display:none;" data-reactid=".hbspt-forms-0.5.0"></div>
    <div class="actions" data-reactid=".hbspt-forms-0.5.1"><input type="submit" value="Submit" class="hs-button primary large" data-reactid=".hbspt-forms-0.5.1.0"></div>
  </div><noscript data-reactid=".hbspt-forms-0.6"></noscript><input name="hs_context" type="hidden"
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POST https://forms.hsforms.com/submissions/v3/public/submit/formsnext/multipart/3400903/231b4ddf-3a26-4f9a-8567-63b0d51ca2a0

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NEXT-BEST-ACTION WITH CAUSAL AI

Enterprises don’t want to merely predict the future, they want to influence it.
Action optimization is one among a set of powerful Causal AI tools that enable
organizations to do just that. Action  optimization identifies the most
efficient action plans you need to take to attain your objectives. We illustrate
how it can augment business intelligence.

Download White Paper
16 May 20225 min read


Enterprises don’t want to just predict the future, they want to shape it. At
causaLens, we build solutions that enable organizations to make data-driven
decisions that optimally shape their future, going beyond mere predictive
analytics. These solutions are powered by Causal AI, a new category of machine
intelligence that can reason about the world the way humans do, by analyzing
cause-effect relationships.

Action optimization is one among a set of powerful Causal AI tools that enable
organizations to make more effective plans and better decisions. Action
optimization identifies the most resource-efficient combinations of actions that
you need to take to reach your goals. We illustrate how it works with a specific
example from sales and marketing.





ACTION OPTIMIZATION — AN EXAMPLE

Suppose that your marketing team has identified that one of your business’s key
demographics (18-25 year-olds) are at risk of “churning” (abandoning the
service). If you can increase retention by just 5% that is likely to translate
into an increase in profits of 50%.

So what can you do about it? You have several options: implement targeted sales
outreach, run ads (on smart TV, paid search, out-of-home, social media, and so
on), discount prices, or offer promotions. Which of these actions should you
take, and how much should you invest in each?

This is where action optimization comes in. There are several constraints that
the technique factors in. 

Firstly, Causal AI is capable of ruling out courses of action that aren’t
feasible or possible. For instance, whilst macroeconomic factors like GDP do
significantly influence customer retention (think of Netflix’s recent
announcement of massive subscriber losses, partly caused by inflation and the
cost-of-living crisis) — but a business can’t meaningfully change GDP. 

The machine then evaluates the feasible actions based on how reasonable and
cost-efficient they are. For instance, giving away the service for free may
induce the customer to renew but is not economically viable for the business —
reaching out to the customer with a targeted sales call may achieve the same
result for a fraction of the cost. Generally speaking there are lots of ways to
persuade a customer to renew (in the technical AI literature this is sometimes
called “crossing a decision boundary”). Causal AI can search through these
actions to find the most efficient pathway across the decision boundary. 

Finally, Causal AI can uniquely factor bespoke business constraints into its
recommendations. Perhaps for strategic reasons you can’t discount prices by more
than 10%, because doing so would undermine customer confidence in your pricing
and compromise margins. No problem — action optimization will take account of
these hard constraints. 

The causaLens platform allows all users in the enterprise to optimize actions at
the click of a button.


CORRELATION-BASED ML CANNOT OPTIMIZE ACTIONS

This type of optimized action planning is only possible with a Causal AI model.
Current correlation-based machine learning does not capture relationships
between different actions, simply assuming that they do not interact with and
impact each other at all. More formally, this “independence” modeling assumption
requires that a change in one feature has no effect on the value of another.

However, we know this is rarely the case in practice. An ML system may be able
to spot that an increase in the overall number of customer interactions is
likely to influence a customer to renew. But it misses the obvious fact that an
increase in the volume of sales calls or emails result in an increase in
interactions, and so it is liable to naively recommend needless additional
actions (call and email the customer).

Causal AI understands the dependencies between features, and hence it can
anticipate all the complex downstream effects of a given course of action.
Correlation-based ML has to assume that all actions are independent of each
other, a clearly unrealistic modeling decision.

It’s generally incorrect to assume that actions are independent of each other —
correlational machine learning models that assume independence recommend
disproportionate (and therefore expensive!) courses of action. 

Traditional correlation-based methods can only show the business or user what
variables would need to change to achieve their goals, but not how to act.
Causal AI provides the best actions for reaching business targets and helps the
user to better understand their problems. 

We have illustrated how action optimization works by focussing on a marketing
example. But the technique applies generally to a very wide range of use cases.
To name a few examples: we have applied action optimization to help consumers
understand their options when they’re refused a mortgage; the technique
facilitates complaint resolution for victims of fincrime and fraud; and in a
very different context altogether, action optimization has been applied to
reduce vaccine hesitancy during the COVID-19 pandemic. 

Our experts will be happy to discuss your business use cases and explore how
Causal AI can address your business challenges.. 
Action optimization builds on a new technique known as “algorithmic recourse” in
the technical AI literature. For further technical information, please see our
tech report on algorithmic recourse.



--------------------------------------------------------------------------------

causaLens provides next-generation AI for the enterprise. Our technology
harnesses Causal AI to build models that are not just accurate but are truly
explainable too, putting the “cause” in “because”. Find out how we can help you
to better understand and explain your business environment.




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