Exercising AI’s capabilities with customer data

    Exercising AIs capabilities with customer data

    Marketing leaders believe that customer data is the most sensitive and valuable data possessed by an enterprise

    Customer data plays a significant role in predicting their potential actions. Historical information related to browsing patterns, buying behavior, and demographics play a significant role in identifying future trends. It also gives the required data on what purchase may be made and how much be paid.

    It provides data that helps them deploy the process of right customer, right message and right time, for use by the brand. As a result, the client enjoys a highly customized and rapid journey from the searching to purchase. The enterprise thus easily converts sales and can increase customer loyalty.

    The new normal has put enterprises’ traditional measures to a test. They are now trying to develop new methods for identifying the client’s needs and changed purchase patterns. More and more brands are pushing for adopting customization across all digital touch points.

    The personalization measures have yielded positive results as customer engagement has increased compared to earlier, along with a trend for repeat purchases.

    Marketing leaders acknowledge that brands have increasingly turned to AI to maximize customer data value and develop customized digital experiences at scale. AI and ML tools adoption has also increased with relevance to marketing strategies.

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    Organizations have observed improved and positive ROI results after the adoption of AI tech. CMOs warn that marketers should be wary of the AI bias when using AI tools, and are prepared to mitigate potential utilization of the software for wrong reasons.

    The pitfalls of AI

    CMOs are aware of the potential dangers associated with deploying AI tools in marketing. They point out that it should be the workman who should be blamed and not the tool. AI is no exception to this rule.

    It has created many opportunities for both organizations and clients. However, when misused, it results in problematic marketing practices that can result in discriminatory results.

    Price discrimination is one such feature. This feature is also known as personalized or dynamic pricing. It involves different people being charged differently for goods. The AI tool would recommend a particular client some product online; the price gets adjusted automatically based on the data the tool has about the client.

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    The customer data includes location, IP address, historical purchases, and browsing history. It may also store more personal data like education level, gender, occupation, age, and race.

    Marketing leaders say that since the tech is trained by data fed by humans, it may often reflect accidental discriminatory practices. For example, an organization may use a particular area’s majority ethnicity and decide on the contract terms to be offered to a potential client.

    Such sensitive data may be used to ensure higher revenue, but this is discriminatory. C-suite leaders believe that the best way to avoid such situations is by not using demographic patterns.

    Owning the responsibility of using AI tech

    CMOs point out that while customizing digital experience built on clients browsing behavior and characteristics has shown increased engagement metrics and higher conversion rates. However, the evolution of tools, method of content modification, and the type of content that the enterprise tries to modify has also changed. AI-fueled tools are needed to accomplish these processes at the scale that is currently required.