Companies are utilizing artificial intelligence for advertising and content-related activities these days. In addition, it also assists with segmentation of the customers and forecasting of revenues. However, this software can provide some wrong advice if this data is outdated or the configuration is improper.
However, an audit helps with that
issue. It seems to be able to detect the mismatched customer segments and
increase the relevance of the messages for the customers. Then there is the
budgeting and that makes it possible to cut down on ineffective advertising and
redistribute the finances properly.
Conversion rates get checked too
by finding weak spots in how people move through the process. Data accuracy
comes up as well since it makes sure the info used for choices is solid. Maybe
some companies see bigger gains than others depending on how they started.
What Is an AI Audit in Digital Marketing?
This is a structured review of
the artificial intelligence technologies within your marketing. It looks at the
quality of data, accuracy of models, automation logic, security and business
impact.
For example if you are using AI
within your eCommerce platform to provide product recommendations and optimise
your advertising campaigns then an AI audit can show if they are providing the
right recommendations to help drive sales.
It can also show any problems
that may be hidden within typical reportage like inaccurate lead scoring or
campaigns that are driving traffic but not quality leads.
The main goal is to link AI
activity to actual business impact.
Why Should Businesses Audit Their AI
Marketing Tools?
Automation systems have reduced
complexity in business operations. These systems' effectiveness relies heavily
on data quality and the systems themselves being correctly configured.
Imagine a firm has an AI model
that uses website activity and email correspondence to score prospects. The
model ignores customers that have already purchased from the firm.
If this model is deployed,
qualified prospects will increase, but sales will not.
In such a case, the model can be
updated and the issues corrected after an audit.
A few examples of problems that
can be addressed after an audit:
- Improper
segmentation: customer groups do not account for purchase activity.
- Unreliable
analytics: the performance metrics reports are inaccurate because of the
incorrect recording of events and faulty event attribution.
- Improper
campaign automation: automation processes optimize toward the recording of
irrelevant conversions.
- Improperly
generated marketing material: the AI generates materials with spelling and
grammatical errors, or conflicting messages.
- Privacy of
customer data is violated when marketing platforms fail to implement proper
controls.
If a company is able to address
all of the above, they will maximize the return on the technology they already
have.
Which Marketing Areas Benefit Most From an AI
Audit?
An AI audit provides the greatest
value when it focuses on systems that influence revenue, acquisition costs, and
customer retention.
Paid advertising
While AI driven advertising
platforms offer automated bid, targeting and budget functionality, they may not
align with your business goals.
In some examples you can have
high volumes of newsletter sign-ups but little conversions.
By auditing conversion events,
attribution models, audience overlaps and campaign objectives, you can ensure
your marketing team optimise your campaigns with the correct rules to improve
valuable customer actions.
Customer segmentation and personalization
AI segmentation can segment your
customers based on their preferences, behaviors and forecasted value.
But if your models are out of
date you might end up making recommendations that aren’t relevant to your
customers, or putting them in the wrong segments.
Auditing can identify issues with
your customer data, recommending the right products and personalization.
For example, if your online store
has a recommendation engine that recommends items they have already bought.
Updating your customer information and rules for recommendations can help make
your offers more relevant.
SEO and content marketing
AI tools can help when performing
keyword research, creating content and writing copy but will be determined by
the intent of the search, how accurate and well written it is.
During an audit you can identify
pages which are covering similar topics, incorrect information and pages that
aren’t converting.
Compare your organic traffic to
engagement and conversion rates.
This can help marketers identify
what content to focus on to attract potential customers.
Email marketing
AI email tools will predict when
to send emails, personalise and identify high-value customers.
An audit should be used to ensure
these predictions are helping improve engagement and retention.
For example, we may find that the
automated retention emails are being sent too late after they've cancelled
their subscriptions.
Improving the data sync and
automation conditions will help ensure you're contacting them at the right
time.
How Can an AI Audit Improve Marketing ROI?
AI audits identify wasted money
and lost revenue. Inaccuracies in an AI's predictions are another major
concern.
An AI audit starts by setting a
performance benchmark.
|
Marketing metric |
What an AI audit examines |
|
Return on
ad spend (ROAS) |
Campaign
allocation and conversion accuracy |
|
Customer
acquisition cost (CAC) |
Audience
quality and advertising efficiency |
|
Conversion
rate |
Personalization
and customer journey issues |
|
Customer
lifetime value (CLV) |
Purchase
forecasts and retention predictions |
|
Lead-to-customer
rate |
Lead-scoring
accuracy and sales results |
Imagine you have a SaaS business
which has a monthly spend of $40,000 on digital advertising. Your AI model is
showing a preference for leads that access free content, but they are rarely
asking for demos.
A review shows these leads are
not matching up to the score given, so changes are made to how the leads are
qualified. Some of the budget is shifted to target higher value audiences.
The team can now look at the cost
per acquisition, conversion rate and revenue against their previous results.
This will help determine if your
changes to your AI are providing real value to your business.
How Do You Conduct an Effective AI Marketing
Audit?
A successful AI audit follows a
structured process, from goal definition to performance validation.
Step 1: Define business objectives
Begin by looking at the areas of
your marketing that are most impacting on your bottom line.
This may be high customer
acquisition costs, lack of personalisation, conversion rates falling or
inaccurate forecasting.
Note down which AI tools you’re
using and the objectives you want to measure.
Step 2: Examine data quality
Your AI systems will need
accurate inputs.
Analyse your customer database,
conversion data, product catalogue and marketing integration.
Check for duplicate customers,
missing transactions, mismatched identifiers and out of date information.
Missing sales data within your
advertising platform will mean the algorithm will prioritise low value leads.
Step 3: Evaluate AI model performance
Compare AI predictions with the
real-world data of your customers.
Evaluate lead scoring models by
looking at the relationship between predicted and real sales quality. Evaluate
recommendation engines by looking at relevance and conversion.
Use the results to identify
accuracy issues by looking at different customer segments and time frames.
This will help you decide whether
you need to feed in more data, change the model setup or retrain.
Step 4: Review security and privacy
Marketing AI platforms will
frequently handle customer data. An audit of who has access, how long data is
stored, third party connections and any privacy requirements should be
undertaken. Ensure that staff are not leaking any sensitive information via
external AI platforms.
Step 5: Create an improvement plan
Prioritize your findings based on
business impact, effort to implement, and risk.
Ensure that high-priority issues
(e.g., conversion event errors, unreliable data) are addressed early.
If you have custom AI models or
complex Integrations within your business then you can get expert assistance to
help you. We offer ai auditing solutions which will help
ensure your data is reliable, your models are performing well, secure and
governed. Recommendations will be provided to help improve your AI.
Recommendations must include who
is responsible and how they’ll know they’ve been successful.
What Can Businesses Learn From Real AI
Projects?
For example, at Cleveroad we
implemented an AI-based provider matching system for a digital health company
in the US.
Previously, users were unable to
easily find compatible healthcare providers because they had to apply various
medical filters and understand medical jargon.
We created a conversational AI
engine that could understand users' natural language and return appropriate
results.
As reported in their case study,
this reduced search drop off to 12% (from 47%) and helped increase completed
appointments by 18%.
Whilst this is an AI development
project, it shows how looking at customer actions and conversions can give
great insights into whether your AI is working effectively.
This can be applied to any search
function within an eCommerce environment, recommendation systems or targeted
marketing campaigns.
What Common AI Audit Mistakes Should
Companies Avoid?
The value of the audit is minimal
if business owners pay attention to technical performance only.
Several issues make its execution
less effective:
Checking tools individually.
Different marketing systems share data, which means that one integration issue
could cause problems for many AI applications.
Relying on old models. The
customers' behavior evolves and models need regular assessment.
Taking advice of automated programs
without any verification. Suggestions provided by platform must help to achieve
certain business goals.
Neglecting the privacy settings.
It is necessary for organizations to know how the customer data is collected,
accessed, and processed.
Not following suggestions made.
Audit requires specific tasks, deadlines, and responsible team members.
It is crucial to make a
prioritization list of problems that must be fixed.
How Should You Measure Results After an AI
Audit?
Evaluating post-audit results
requires concentrating on initial objectives of the business.
In terms of improving customer
acquisition, the comparison of CAC and the rate of lead-to-customer conversion
can be carried out before and after making changes.
Regarding personalization,
measurement of product recommendations interaction, conversion rates, and
revenue per visitor is important.
Controlled experiments can
contribute to more effective proof.
For instance, an eCommerce
company may conduct an A/B testing for its new recommendation system compared
to the previous one.
Other technical metrics include
accuracy of predictions, data up-to-date level, and system error rates.
When Should Businesses Conduct an AI
Marketing Audit?
An audit for artificial
intelligence is especially beneficial in situations where marketing performance
falls despite no obvious reason being found.
Such signs include increased cost
of acquiring users, discrepancies within analytics data, decreased conversion
rates, and inaccurate predictions of customers' actions.
An audit might also be considered
by companies before making significant changes to their AI system or making new
large investments.
For companies that have already
implemented the process, regular audits can be planned depending on the
complexity of the model used.
Final Thoughts
AI will be able to enhance the
efficiency of digital marketing when it works with precise data and achieves
definite business goals.
Conducting an AI audit allows
organizations to determine where they lack automation, enhance their customer
segmentation, and correct flaws in their marketing technologies.
The best way to do it is to begin
with the business issue, assess the systems used for AI, and analyze the
results after their implementation.