Connect with us

Artificial Intelligence

How Startups Can Formulate Data-Driven Marketing Strategies Using AI




We have talked a lot about the benefits of big data in marketing. The global marketing analytics market was worth $2.1 billion in 2019. This figure is expected to rise sharply in the future as more companies are likely to discover the benefits data-driven marketing affords.

Understanding the Benefits of Data-Driven Marketing

You have launched your startup. But do not think that there is nothing left for you to do. There are many things for you to do to run and grow your startup.

Regardless of how good your product is, you have to create an audience and awareness about your product. It is where the role of digital marketing comes into play. And Artificial Intelligence (AI) is creating new possibilities in the digital marketing space.

In today’s competitive business landscape, you cannot undermine the power of AI in shaping your marketing strategy to stay ahead of your competitors. Among other things, data-driven marketing can be great for driving higher conversion rates.

Suppose you own a startup and work on formulating your digital marketing strategy. In that case, you should understand what data-driven marketing is and how you can leverage AI capabilities for structuring your strategy.

What is a Data-Driven Digital Marketing Strategy?

Last year, we talked about the benefits of using big data to drive sales. There are a lot of other ways to utilize big data in your broader marketing strategy as well.

Adopt a practical approach while formulating your digital marketing strategy. Take into account the realistic factors, and refrain from developing your strategy solely on assumptions. If you base your strategy on assumptions about your product or audience, it can lead to a faulty strategy that will entail no result.

So, adopt a data-driven approach. Gather data as much as possible and derive insights from the data. Subsequently, make decisions based on the quantitative insights.

Any formulation of digital marketing strategy involves four steps, which are:

Market Research

Carry out thorough market research, examining your target customers, competitors, and preferences of your target audience. Also, study your competitors carefully. Find out the consumers they target, marketing strategies they implement, and messages that go well with consumers.

Optimize Buyer Journey

You cannot expect your target customers to buy your product as soon as they see it. No consumer will instantly buy your product, regardless of the best marketing strategy you put into action. 

By and large, buyers go through the following phases in their buying journey:

  1. They know about your products.
  2. They understand its benefits.
  3. They figure out what problems it could solve and compare alternative products.
  4. They decide whether to buy it or not.

During your digital marketing formulation, find out ways to optimize the buyer journey with big data.

Choose the Right Digital Marketing Channels

Select the digital marketing channels carefully to implement. You can include email, content, SEO, and social media marketing.

Set Your Strategy in Motion

Finally, set your digital marketing strategy in motion after having a try-out phase. Subsequently, track its performance and make the necessary adjustments.

Role of AI

AI has taken digital marketing to the next level. With substantial data-driven capability, AI can quickly gather and process vast amounts of data. It helps to derive deep insights into complex data.

What is more, is that AI can automate processes. There is no need for your marketing team to research content as smart algorithms can accomplish the task faster, saving time.

You have now come to know that AI has unleashed a revolutionary trend in the digital marketing space. Here is a comprehensive guide on leveraging the power of AI in creating a robust digital marketing strategy:

Know Your Customers

The first step in crafting an impactful digital marketing strategy is to know who can be your customers and their preferences. With smart algorithms, AI can recognize patterns in the market research data that can elicit macro-scales patterns to provide actionable insights.

Additionally, you can run AI-based algorithms to extract massive amounts of customer-centric data from social media or discussion forums. The algorithms can crawl through numerous posts, reviews, and articles to draw out the hidden sentiments.

With such sentiment analysis, you can identify the major questions, trends, and gaps corresponding to your product or service. Moreover, you can also get the demographic details of your target audience.

Say you are running a survey to elicit an in-depth understanding of your target customers’ tastes and preferences, then AI can help you attain your objective better. With the voice recognition capability, AI can even transcribe and analyze oral discussions.

Competitor Research

You cannot formulate any digital marketing strategy without your competitors’ adequate knowledge. AI-based tools can give you unique advantages in your competitor research. With AI tools, you can track competitors on social media, know the posting frequencies, types of posts, and identify high-performing content. In this way, you can figure out the strategies your competitors are implementing.

Furthermore, you can also get deep insights into the demographics of your competitors’ followers and their interactions with brands, shares, and likes.

After you launch your website, you have to monitor the page traffic and bounce rates. It will help if you study your competitors’ websites to determine what you should do to enhance traffic to your website and lower bounce rates. AI-based tools can help you reverse-engineer your competitors’ website analytics.

Moreover, AI-based tools can extract your competitors’ link profiles to enable you to know which page links your competitors put on their websites. As such, you can draw up a page linking strategy for your website to stay competitive.

You need to have links to your website on other web pages, and it is a critical factor for high rankings in search engines, which see links as proof of trustworthiness and authority.

Buyer Journey Optimization

You can use AI-based tools to get a good idea about your customers’ buying journey and improve their experience.

Today, AI has become the main factor behind excellent user experience (UX). And with Machine Learning algorithms, AI tools can give your target customers a personalized experience. The tools can keep a tab on your target customers from the first time they come in touch with your brand to eventually purchase your product.

You will get to know at which stages of the buyer journey your customers are and what features they are looking for in your products. Besides, you will also get to know the problems they are trying to solve.

With the valuable insights, you can curate your website’s content, adjust paid advertisements or send personalized messages.

You can also use AI for personalization, which is crucial for attracting consumers to your brand. Statistics show that there is a probability that 91 percent of consumers are likely to purchase products of companies that send personalized content, conforming to their interests.

AI tools also can improve your customer service to keep your customers happy. It is also an essential part of the buyer journey, especially during comparing and deciding whether to buy the product. With good customer service, you can get customers quickly.

Chatbots are AI-based tools that can answer website visitors’ questions about products and services. It can free your employees from customer handling to essential tasks.

Content Marketing

It is often said that in digital marketing, content is the king. Creating the right content is crucial to its success in any digital marketing strategy.

When it comes to SEO, search engines favor websites with high-quality content. With AI implementation, you can create high-quality content that will drive more traffic to your website.

AI tools mainly consider your target audience’s interests and your competitor’s performance to generate content that resonates with potential customers.

AI tools also can relieve you of researching to write content. The tools scan through all content on the given topic and provide you with valuable insights to structure your content. Moreover, the tools can also improve your content’s quality by polishing the grammar and helping you with the right vocabulary.

Social Media

AI-based tools improve your social media performance by analyzing your past social media performance and suggesting the right content to post. You also get cross-platform social media insights through the tools.


Digital marketing and AI nowadays go hand in hand. With AI, you can craft a more impactful strategy to promote your products better online and stay ahead in the grueling competition.

The post How Startups Can Formulate Data-Driven Marketing Strategies Using AI appeared first on SmartData Collective.

Checkout PrimeXBT

Artificial Intelligence

Prioritizing Artificial Intelligence and Machine Learning in a Pandemic




AI and ML
Illustration: © IoT For All

Artificial Intelligence (AI) and Machine Learning (ML) give companies the one thing humans can’t – scalability. Over time, humans limit a businesses’ ability to scale; they can only work so many hours at a given efficiency. On the other hand, AI and ML can work around the clock with the sole focus on a given project. As organizations navigate through COVID-19’s impact and the future of a remote workforce, scalability and efficiency can be the key to an organization’s successful recovery.

Implementation Challenges

The benefits of AI and ML don’t come without their own challenges; however, the top challenges are a lack of skills and time for proper implementation. In July, Deloitte found in a survey that 69% of respondents said the skills gap for AI implementation ranged from moderate to major to extreme. Simultaneously, many companies overlook the investment it takes to build the processes and infrastructure needed for successfully training, testing, deploying, and maintaining AI and ML in their enterprise.

Such challenges often cause companies to de-prioritize AI and ML projects, especially in times of uncertainty. That has been increasingly obvious throughout the COVID-19 pandemic. But while some organizations have drawn back on their efforts, the current global state demands the greater need for AI and ML to support critical business processes. This is especially true today given the growing remote workforce, considerations for returning to the workplace and work happening in silos worldwide.

Though challenging, it is not impossible to properly implement AI and ML. In this evolving COVID-influenced business landscape, four steps are key to effectively implementing a strong AI and ML system that helps streamline critical business processes despite uncertainty and limited resources.

Identify the Problem to Be Solved

Some companies mistakenly view AI and ML projects as a ‘silver bullet’ to solve all their problems. This often results in overinflated expectations, an unfocused approach, and unsatisfactory results. Instead, companies should identify those specific problems that will have the biggest impact from implementing AI and ML solutions and be hyper-focused on solving those problems.

Select Your Data

The second step in creating a strong AI and ML algorithm is to select the source data that your algorithm will be training on. There are two main options: training on your own data or training on a larger scale data set. Based on experience, training your algorithm on your own data puts you at a disadvantage. By training on a larger scale data set, the likelihood of success increases because your data is more representative and varied. Through advanced concepts such as transfer learning, companies can use semi-trained models based on larger data sets and then train the “last mile” using their own specific content unique to their business.

Clean House

The standby rules of data management apply here – garbage in, garbage out. Ultimately, the quality and accuracy of machine learning models depend on being representative. AI and ML – fed with the right data – can streamline operations and increase the benefit of companies’ DX and cloud migration journeys.

When you’re kicking off an AI or ML project, the most critical step is to clean up the data that your algorithm will be training on, especially if you’re using your own data or models.

Make Room for Training

AI and ML are all about probability. When you ask it a question, for example, “Is this a cat?,” the results you receive are the algorithm saying, “Out of the three buckets I was trained on, the likelihood of this image being a cat is .91, the likelihood of this image being a dog is .72 and the likelihood of this image being a bird is .32.”

This is why training on varied data is so important. If your training data only includes images of cats, dogs, and birds and you ask the algorithm to analyze the picture of a crocodile, it will only respond based on the buckets it’s been trained on – cats, dogs, and birds.

If you’ve properly selected and cleaned your data, training should be an easy last step, but it’s also an opportunity to go back to the first two steps and further refine based on your training.

The front end of training an AI and ML algorithm can be time-intensive, but following these four steps can make it easier to achieve significant outcomes. Across industries, AI and ML can quickly show ROI. For example, in the insurance industry, AI and ML can help insurers quickly search contracts, so employees aren’t sifting through contracts and repositories around the globe to answer simple questions. This means time efficiencies for an industry that COVID-19 has heavily impacted.

Even better, working with a SaaS provider with experience in your industry can make this process much easier and less costly. SaaS platforms allow companies to take advantage of having all of the infrastructure, security, and pre-trained models in place to reduce the overall effort and time to value. Many platforms allow users to uptrain the predefined models with unique customer data, reducing the training effort needed for model creation. Companies can then focus on integration with their ecosystem and workflows rather than model creation itself.

Bigger Picture

Overall, businesses can soften the impact of COVID by focusing on the bigger picture with AI and ML. Implementing AI and ML projects increase business productivity despite these times of uncertainty. As we continue on the road to recovery, we need tools like AI and ML to stay focused on the bigger picture, mission-critical tasks.

Coinsmart. Beste Bitcoin-Börse in Europa

Continue Reading

Artificial Intelligence

Fintech and AI Task Forces Reauthorized for 117th Congress by House Committee




Congresswoman Maxine Waters, Chairwoman of the House Financial Services Committee, has announced the reauthorization of the Financial Technology [Fintech] and Artificial Intelligence [AI] Task Forces for the 117th Congress.  These two Task Forces are relatively new having emerged in the 116th Congress in 2019. The two entities seek to provide a forum for innovation in financial services.

Once again, Congressman Stephen Lynch will chair the Fintech Task Force and Congressman Bill Foster will chair the Task Force on AI.

Chairwoman Waters issued the following statement:

“I am very pleased to announce that our Task Forces on Financial Technology and Artificial Intelligence will continue their work examining emerging technologies in the financial services and housing industry. These Task Forces will investigate whether these technologies are serving the needs of consumers, investors, small businesses, and the American public, which is needed especially as we recover from the COVID-19 pandemic. I will reiterate that Congress must promote responsible innovation, as well as ensure that regulators are able to properly oversee this rapidly changing environment. The continuation of the Task Force on Financial Technology, led by Congressman Lynch, and the Task Force on Artificial Intelligence, led by Congressman Foster, will allow Congress to be aware of all cutting-edge developments in our space. Furthermore, the hearings and legislation from these Task Forces will make sure policy can keep up with the changes to our financial services, and do its part to make sure technology is not being used to discriminate or exacerbate existing biases under the guise of innovation.”

Members of the Task Forces will include both Republic and Democrat Representatives.

Currently, no meetings have been scheduled.

Coinsmart. Beste Bitcoin-Börse in Europa

Continue Reading

Artificial Intelligence

Micromobility’s next big business is software, not vehicles




The days of the shared, dockless micromobility model are numbered. That’s essentially the conclusion reached by Puneeth Meruva, an associate at Trucks Venture Capital who recently authored a detailed research brief on micromobility. Meruva is of the opinion that the standard for permit-capped, dockless scooter-sharing is not sustainable — the overhead is too costly, the returns too low — and that the industry could splinter.

Most companies playing to win have begun to vertically integrate their tech stacks by developing or acquiring new technology.

“Because shared services have started a cultural transition, people are more open to buying their own e-bike or e-scooter,” Meruva told TechCrunch. “Fundamentally because of how much city regulation is involved in each of these trips, it could reasonably become a transportation utility that is very useful for the end consumer, but it just hasn’t proven itself to be a profitable line of business.”

As dockless e-scooters, e-bikes and e-mopeds expand their footprint while consolidating under a few umbrella corporations, companies might develop or acquire the technology to streamline and reduce operational costs enough to achieve unit economics. One overlooked but massive factor in the micromobility space is the software that powers the vehicles — who owns it, if it’s made in-house and how well it integrates with the rest of the tech stack.

It’s the software that can determine if a company breaks out of the rideshare model into the sales or subscription model, or becomes subsidized by or absorbed into public transit, Meruva predicts.

Vehicle operating systems haven’t been top of mind for most companies in the short history of micromobility. The initial goal was making sure the hardware didn’t break down or burst into flames. When e-scooters came on the scene, they caused a ruckus. Riders without helmets zipped through city streets and many vehicles ended up in ditches or blocking sidewalk accessibility.

City officials were angry, to say the least, and branded dockless modes of transport a public nuisance. However, micromobility companies had to answer to their overeager investors — the ones who missed out on the Uber and Lyft craze and threw millions at electric mobility, hoping for swift returns. What was a Bird or a Lime to do? The only thing to do: Get back on that electric two-wheeler and start schmoozing cities.

How the fight for cities indirectly improved vehicle software

Shared, dockless operators are currently in a war of attrition, fighting to get the last remaining city permits. But as the industry seeks a business to government (B2G) model that morphs into what companies think cities want, some are inadvertently producing vehicles that will evolve beyond functional toys and into more viable transportation alternatives.

The second wave of micromobility was marked by newer companies like Superpedestrian and Voi Technology. They learned from past industry mistakes and developed business strategies that include building onboard operating systems in-house. The goal? More control over rider behavior and better compliance with city regulations.

Most companies playing to win have begun to vertically integrate their tech stacks by developing or acquiring new technology. Lime, Bird, Superpedestrian, Spin and Voi all design their own vehicles and write their own fleet management software or other operational tools. Lime writes its own firmware, which sits directly on top of the vehicle hardware primitives and helps control things like motor controllers, batteries and connected lights and locks.

Coinsmart. Beste Bitcoin-Börse in Europa

Continue Reading


EU proposes strict AI rules, with fines up to 6% for violations




Join Transform 2021 this July 12-16. Register for the AI event of the year.

(Reuters) — The European Commission on Wednesday announced tough draft rules on the use of artificial intelligence, including a ban on most surveillance, as part of an attempt to set global standards for a technology seen as crucial to future economic growth.

The rules, which envisage hefty fines for violations and set strict safeguards for high-risk applications, could help the EU take the lead in regulating AI, which critics say has harmful social effects and can be exploited by repressive governments.

The move comes as China moves ahead in the AI race, while the COVID-19 pandemic has underlined the importance of algorithms and internet-connected gadgets in daily life.

“On artificial intelligence, trust is a must, not a nice to have. With these landmark rules, the EU is spearheading the development of new global norms to make sure AI can be trusted,” European tech chief Margrethe Vestager said in a statement.

The Commission said AI applications that allow governments to do social scoring or exploit children will be banned.

High risk AI applications used in recruitment, critical infrastructure, credit scoring, migration and law enforcement will be subject to strict safeguards.

Companies breaching the rules face fines up to 6% of their global turnover or 30 million euros ($36 million), whichever is the higher figure.

European industrial chief Thierry Breton said the rules would help the 27-nation European Union reap the benefits of the technology across the board.

“This offers immense potential in areas as diverse as health, transport, energy, agriculture, tourism or cyber security,” he said.

However, civil and digital rights activists want a blanket ban on biometric mass surveillance tools such as facial recognition systems, due to concerns about risks to privacy and fundamental rights and the possible abuse of AI by authoritarian regimes.

The Commission will have to thrash out the details with EU national governments and the European Parliament before the rules can come into force, in a process that can take more than year.

($1 = 0.8333 euros)


VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative technology and transact. Our site delivers essential information on data technologies and strategies to guide you as you lead your organizations. We invite you to become a member of our community, to access:

  • up-to-date information on the subjects of interest to you
  • our newsletters
  • gated thought-leader content and discounted access to our prized events, such as Transform 2021: Learn More
  • networking features, and more

Become a member

Coinsmart. Beste Bitcoin-Börse in Europa

Continue Reading
Esports5 days ago

Apex Legends Season 9 will add new hero, fix Banglore bugs

Esports5 days ago

Code S: Trap & Zest advance to RO8, playoff bracket set

Blockchain3 days ago

Mining Bitcoin: How to Mine Bitcoin

Fintech4 days ago

Fintech offers brokers better commissions after BID

Esports4 days ago

OWL 2021 Power Rankings – #9 Guangzhou Charge

PR Newswire3 days ago

Hello Pal Signs Definitive Purchase Agreement to Acquire Interest in Dogecoin/Litecoin Mining Assets

Esports4 days ago

xQc Banned From NoPixel GTA RP Server Once Again

Blockchain5 days ago

Stanislovas Tomas im Interview: „NFTs können unsere Gesellschaft verändern“

Esports4 days ago

CDL Challengers Elite Stage 3 Preview

Coinbase hourly chart
Blockchain3 days ago

Coinbase Addresses Future Revenue Concerns With Plans to Become Crypto’s Amazon

Esports5 days ago

Zayt Retires From Competitive Fortnite For The Second Time

Esports4 days ago

Three takeaways from the SWT Japan Ultimate Online Qualifier

Esports4 days ago

Twitch streamer Lando Norris takes Italian F1 Grand Prix podium

Esports4 days ago

Cloud9 Perkz says Kassadin can’t ever be balanced in LoL

Esports5 days ago

Dota 2: DPC Weekly Recap — SEA Apr 12-17, 2021

Blockchain3 days ago

Did Coinbase Insiders Really Cash Out? It’s Complicated

Esports4 days ago

Valorant: Meet the top 4 EU teams qualified for VCT EMEA Stage 2 Challengers Final

Fintech4 days ago

HashChing acquires Mystro to further expand its offering to mortgage brokers

Esports5 days ago

Nigma make impressive debut versus Team Secret in Europe Season 2 DPC league

Esports5 days ago

Was Jake Paul’s first-round knockout of Ben Askren rigged?