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AI Applications in Marketing and Finance

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January 10, 2022
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AI Applications in Marketing and Finance

by ITECHNEWS
January 10, 2022
in Data Science, Leading Stories
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AI Applications in Marketing and Finance

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Artificial intelligence has become an integral part of business solutions. What AI applications can professionals utilise to achieve success?

Some people probably still think about AI from a dystopian perspective where cars fly or robots rule the world. In a way, these are already in the works. We have self-driving cars, while vacuums are getting smarter to sweep dust independently. More than that, almost every industry is diving into automation and artificial intelligence—specifically, marketing and finance.

Gone were days when marketing was all over print ads, and computing was through manual calculators. Almost everything is digital now. Marketers make use of big data and algorithms to power up digital campaigns. Financial analysts can compute a large set of variables and data in a click.

Both industries utilise AI in their day-to-day transactions. From accommodating consumer requests and boosting productivity to protecting customer data and maintaining a secure database—artificial intelligence has revolutionised the routinary tasks in the marketing and finance sectors.

With AI technologies, work has been more efficient and (cost) effective. Here are our top AI application picks that are essential in building successful digital campaigns and addressing modern fintech problems.

AI Applications in the Finance Sector

AI has been incredibly useful within the end-to-end processes of the finance industry. It benefits companies, consumers, personal users alike in managing their financial transactions and offering accessible options regarding finances. Some examples are:

Eno

Remember when we used to go to the bank to ask about our account status? Yea, that’s much easier now since the rising need for e-banking. Applications like Eno use NLP or natural language processing that answers your bank queries like how a normal agent would.

Developed by Capital One, Eno is the pioneer in SMS text-based assistants that can answer and predict customer needs proactively. Most banks have also developed AI agents to accommodate questions from their stakeholders 24/7.

DataRobot

Forecasting is an integral part of fintech. It is the basis of a bank’s predictive models patterned to the consumer’s spending habits. DataRobot is essential in draughting these forecasts with its machine learning capability to analyse big sets of data. It can also conclude accurate decisions through credit card activity, lending, and more—making it easier for banks to move forward with data-driven predictions rapidly.

The accuracy that AI brings to the table in forecasting is the key to how banks can keep up with the fast-paced evolution of the fintech industry. Since there is no human element basked in these decisions, algorithms speak for our credit scores with no biases involved.

AI in the Marketing Sector

Building on the relevance of data algorithms in today’s digital landscape, marketers are the ones who can maximise the most out of the benefits of AI applications. The integration of artificial intelligence in marketing strategies and campaigns can affect consumer behaviours and purchasing powers. Some of these tools include:

Persado

AI-powered creativity can sound more human than actual humans. Persado, a creative solutions company uses machine learning in draughting their clients’ copies to become more conversational to their target market.

Made possible through computational linguistics, Persado, among other AI services of its kind can deliver language models and targeted copies that allows brands to personalise the service based on a brand’s tone and messaging, The ability of AI to store large quantities of data and turn them into marketing solutions like this makes it easier for creatives to work efficiently and deliver more outputs in a short time.

MarketMuse

The rise of social media and digitalisation makes brand competition broader. Businesses nowadays compete to become both a household and a top-searched brand. With this, marketers utilise search engines by publishing content based on industry-related topics that can push the brand up to Google’s number one spot.

However, keyword targeting and producing blogs and materials to go up the search ladder is easier said than done. But with MarketMuse, marketers can identify the topics that the brand can target. This eliminates the process of market analysis and research which could take a long time if done manually. MarketMuse streamlines content marketing strategies and upscales organic reach to ensure success.

Reliability of Artificial Intelligence Technology

AI tools can provide businesses with an accurate overview of any industry. There’s no doubt that it can fully transform workflows and provide the best possible solutions to mitigate risks and remove bottlenecks in a project.

However, the reliability of these applications depends on their compatibility with a business model. Applying new technologies to amplify a marketing or finance company’s productivity and digital campaigns should be done with caution and lots of research.

Ensure that the application has gone through a comprehensive beta testing phase to achieve optimum results. It’s also a way to safeguard sensitive business information and customer databases. Remember, one of AI services’ unique selling propositions is to minimise cost while maximising results.

AI powers industry initiatives to achieve tangible success 

At this point, artificial intelligence and machine learning aren’t something that industries consider to be optional. Brands need to work around data-driven strategies that target a large-scale audience to keep up with their competitors.

AI produces tangible results in a single click. It automates and irons out business processes. In the end, it’s how marketers and finance analysts translate these results into campaigns and strategies that will make all the difference

Source: ARLEEN ATIENZA
Tags: AI Applications
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