Leading edge companies use AI, AIOps, and Data Analytics to exceed consumers’ demands for Transparency

Authenticity and honesty are the keys to building trust and trust is at an all time low in the United States.

Americans distrust established institutions including media outlets, government, corporations, and NGOs. Read the 2021 Edelman Trust Barometer report for a comprehensive understanding of the trends regarding public trust over the past 20 years.

It doesn’t stop there, the list is nearly endless.

• We distrust product brand marketing, nutritional, and sustainability claims.
• We believe our technology is “listening”.
• We enacted Sarbanes-Oxley in 2002 to protect shareholders and the public from fraudulent financial practices in corporations.
• We have created a fact checking industry of more than 300 organizations to soothe our distrusting hearts. Hearts that are taught to check facts starting in grade K, according to Frank Baker, author of Media Literacy in the K-12 Classroom. There actually exists an annual Fact Checking Summit.

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Consumers pay more for brands they trust. Building organizational transparency increases consumer trust, loyalty and revenue.

A management team who commits to being transparent with its employees and the public, earning their trust, will find it has an authentic and enthusiastic army of evangelists elevating the company’s image with their friends, family, associates, and social media network, who are also consumers.

Leading edge companies use AI, AIOps, and Data Analytics to exceed consumers’ demands for Transparency.

Here’s how it works in the real world, using an example of an organization making claims about sustainability, “helping the planet and it’s people to live longer, happier lives”. That is a vacuous claim, means nothing, and the organization has just wasted an opportunity to engage precious consumer time and mindshare, and earned no loyalty or trust with the customer. Even worse, the company has earned a negative consumer perception of worthless, deceitful trickery. “Helping the planet” is too vague and how are you measuring “longer, happier lives”?

Consumers need measurable, data-driven or scientific proof to support the claims a company makes. That is transparency. That’s how a company earns trust.

The difference between Data Analytics, AIOps, and AI, and how they each have their own role in driving successful consumer and employee trust.

Data Analytics

Data Analytics use statistical data, observations from the past, to create clear, meaningful insights from which an executive can develop goals, tactics, and objectives.

In our company above, data analytics would be employed to show scientific, and quantifiable progress towards a meaningful sustainable objective. An example would be the precise percentage of reduction in power usage over a precise period of time for manufacturing a specific product, not the whole line of products, category, or company. It’s not enough to claim lower power, it must be measured over a specified period of time for each product for which the label claims are being made.

AIOps

AIOps, are the intelligent systems that identify potential issues and instigate proactive IT solutions before business systems become hindered inoperative. Simply put, AIOps reduce downtime for an organization which improves the customer and employee experience. Gartner predicts that AIOps service usage will rise from 5% in 2018 to 30% in 2023.

In our example, for the company to measure progress on reducing power usage, it must be measured accurately and communicated appropriately throughout the sales and marketing functions to effectively influence its consumers. As consumers are increasingly willing to pay premiums for their pet causes and personal agendas, real time marketing and sales based on accurate measurement will drive real time pricing strategies. Again, consumers will pay premiums for proof of progress and will punish for inaccuracies and misleading information.

AI

AI delivers the early and predictive signals that inform an executive on her consumer’s buying behaviors, preferences, buying frequency, price sensitivity, loyalty, even the consumer’s outside hobbies or interests.

The of-the-moment world in which we live means companies don’t have the luxury of basing future decisions solely on historical data. Way back in 2017, according to Forbes, a consumer was influenced by up to 10,000 ads every day, every 4.3 seconds in a 12 hour day. By the time a company tracks the result of the ad, it’s too late.

In our example, our executive could predict and ensure she is focused on the metric currently important to her consumers. It’s possible that power usage is less important as a sustainability measure to her customer than other initiatives like social issues, carbon or water usage, plastic packaging content, employee well-being, and so forth. As packaging-on-demand becomes mainstream, predicting consumer behavior will increase in value.

The companies who are best at combining data analytics, AIOps, and AI to track, understand, operate, and predict are increasingly agile and relevant to their consumers and employees, who will repay with their trust, loyalty, evangelism, and increased profits.

One last thought, consumers and employees don’t expect perfection, they expect honesty and proven progress. When an inevitable issue arises, earn trust by explaining the situation and the plan for rectification.