Transformation of Digital Marketing through technologies like AI & Data Analytics

With an average startup growing at a rate of 15% and 45%, agility and time to market are critical factors for success. The penalty for coming in second could be grave. With such low error tolerance, adopting artificial intelligence or machine learning algorithms to automate repeatable processes helps businesses to scale rapidly and reduce human errors, thereby boosting productivity

As early as 2020, research suggests that an average human would be creating a digital footprint to the tune of 1.7MB every minute. This would mean more complex datasets with more unique data touchpoints per user. Sifting through these almost cosmic proportions of data points is an arduous task. Fortunately, this is precisely where Digital Marketing stands to benefit from leveraging Data Analytics and Artificial Intelligence.

Marc Benioff of Salesforce sagaciously said, “Speed is the new currency of business.” With an average startup growing at a rate of 15% and 45%, agility and time to market are critical factors for success. The penalty for coming in second could be grave. With such low error tolerance, adopting artificial intelligence or machine learning algorithms to automate repeatable processes helps businesses to scale rapidly and reduce human errors, thereby boosting productivity.

Conversely, imagine a time where businesses solely relied on print media for their marketing efforts. After months of research, planning, and scheduling, an advertisement would be released. For a multitude of reasons, should the Ad be pulled, the loss of investment would be hard to stomach. However, digital, due to its inherently dynamic nature has transformed the conventional marketing landscape. Through a carefully calibrated strategy of investing in strong analytics fundamentals, businesses can make informed strategic decisions, leading to increased ROI and improved shareholder value.

Use of AI to gain a competitive edge
It is next to impossible to break large voluminous data sets through a run of the mill analytics solutions. The challenges that plague most marketers is how to derive specific intelligence over large-scale algorithms built over large datasets to aid in decision making. Businesses that invest time and energy in building solutions of the future stand to gain a competitive edge in this hyper-competitive space of driving strong customer acquisition and marketing outcomes.

These self-learning platforms use AI to identify patterns, spot trends and successfully simulate and predict business outcomes. Marketers can use this intelligence to hone on the right tactics and resources for growing their digital footprint. Taking the instance of paid marketing, advances made in bidding strategies, targeting, and campaign delivery have re-shaped how paid marketing is managed today. Businesses who have invested in innovating themselves stand at the cusp of reaping rewards of being early movers.

Key to enhance Campaign Performance – AI and Data Analytics
Campaign management for paid channels has traditionally required a lot of manual intervention. As a result, brands need to part with a sizeable amount of resources to be spent on daily campaign management. With AI and analytics playing an increasingly dominant role in campaign optimization, marketers are realizing greater efficiencies with fewer funds. AI can automate campaigns based on pre-defined rules, as it is programmatically driven. Marketers may set specific rules for campaigns whose goal is maximizing conversions. The rules are fed into an AI-based system that automatically runs campaigns. By doing so, it helps to meet the objectives. There is little intervention required from campaign managers in executing such campaigns.

Personalization is key
The work of AI has enlarged; it is helping businesses personalize experiences for each customer. It allows businesses to understand prospects’ choices and messages likely to resonate with individuals. This allows brands to promote products through targeted messaging that engages the right kind of customer towards conversions.

Hyper-personalization is the next big thing. Artificial intelligence algorithms can compute many data points on the user, such as historical data, location, market affinity, and past behavior, to tailor a user experience that’s relevant to that one specific user. This, in turn, increases brand affinity for the customer. They are more likely to become recurring customers.

Using AI, brands in any industry may create personalized user experiences. For example, financial brands use personalized experiences to bring customers’ attention to the right credit cards or how Amazon personalizes product recommendations for each customer or with chatbots that emulate actual human interactions.

With the regular use of AI, personalization will become popular, and hyper-personalization will become standard practice. In doing so, brands have the ability to create a unique brand impression for every user that addresses their very specific concerns.

Role of Data Analytics
Data Analytics guarantees agility by leveraging data to make corrections in real-time. In digital marketing, four main analytical models can be deployed — namely, descriptive analytics, inferential analytics, predictive analytics, and prescriptive analytics.

Most agencies and tools use a mix of descriptive and inferential analytics wherein they look at historical data for insights. This would be the first stage in a three-step process. The next step would be to predict the future using past data. This is predictive analytics. Using this method, marketers can assess the effectiveness of their marketing campaigns and drive better results. The final step is to figure out which course of action to take. Using prescriptive uplift modeling, brands can predict the likelihood of users reacting to a certain campaign. Consider this to be a more proactive take at campaign planning. By doing so, brands calculate event outcomes if end users choose a different course of action and proactively be prepared to build in contingencies for a wider variety of demographics. While predictive analytics can build insights based on historical data, it comes down to human intelligence on how we choose to act on these insights. Prescriptive analytics, on the other hand, plans for the factors that influence outcomes.

These advanced analytics methods help marketers make informed decisions and layout a sound strategic roadmap for their brands.

Finally, marketers use attribution modeling as they spend on multiple channels, including print, TV, and radio. Both, advanced attribution and media mix modeling is helpful to the marketers, as these are helpful to quantify each channel’s contribution to a business. The proper understanding of each channel helps the marketers to allocate the budget that maximizes sales and increase the ROI per channel.

The challenge now is to adopt these technologies whilst developing your next marketing strategy quickly, but in order to do so, one must upskill their workforce to work these complex algorithms. With the lines between a data analyst and marketing analyst becoming increasingly blurry, I believe the future marketer would need to be adept in the field of data science and statistics.

Aditya Saxena, Vice President, APAC, iQuanti

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  • aryne

    Thanks for sharing this post