5 Strategies for Leveraging AI In Digital Marketing

In this article, I would like to share with you my 5 strategies of leveraging AI to achieve marketing goals. No matter you aim to drive leads, acquire new customers, you can learn from strategies how AI in digital marketing make you more value and your business success.

AI in digital marketing

In this article, I would like to share with you my 5 strategies for leveraging AI to achieve marketing goals. No matter whether you aim to drive leads, or acquire new customers, you can learn from strategies how AI in digital marketing makes you more valuable and your business successful.

What’s artificial intelligence

Before learning how to leverage, we first must understand how AI works in digital marketing.

Artificial intelligence is a program with a learning capacity, designed to stimulate and replace human beings to handle work. For those repeating physical work and complex math subjects, AI is the best solution.

The key icon of AI is machine learning capacity. The beauty of this capacity is automated, ice ball rolling power, and a self-learning brain. From the slide attached below, machine learning is classified into 3 stages with the related effort we need to input. It’s basic instruction to learn to work with AI. 

  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning

machine learning classification capacity

With its super-fast speed of machine learning such as data calculation and processing capacity, AI is able to perform far better than human beings do. It indeed helps save us time. So we could focus on the work originally we should. It’s like communication, creativity, transformation, and problem resolution.

Take Google marketing platform display dynamic ads for an example.

google marketing platform

  • When we aim to drive B2B event leads from display ads, Google 360 platform AI is not able to understand and help deliver dynamic ads before we segment target audience groups, set up a campaign goal and label the converted event spot.
  • Google 360 display ads are learning the campaign insight based on these given elements, it often causes cost per lead to exceed your goal due to many variable elements increasing dynamic ads learning time cycle, such as device, time, audience demographic, data source, etc. We need to adjust the setting and direct the AI learning model to widen or narrow the scope, for the purpose to manage budget efficiency.
  • After 2 ~ 3 weeks, dynamic ads basically can deliver personally and meet the goal, based on CTR and CPL, etc metrics we can see. At this moment, Google 360 AI can more accurately predict the result and audience size according to our daily budget. Also, AI keeps updating feedback on the correlation between the campaign goal and variable data, like women who are interested in USA politics got a higher form submission rate. It’s nothing right or wrong with AI feedback, so all we need to do is to judge and see if A/B testing is necessary.
  • We need to test useful feedback we think from Google 360 AI because the testing result is a key direction and admit for AI to learn your goal and run automatically on behalf of you step by step. After all, it’s just one scenario that which Google 360 AI delivers the version of the most related ad to the woman who is interested in USA politics automatically. What you are aiming to be 100% confident in is Google AI can automatically use our content assets to dynamically communicate with men, for example, those who hate USA politics.

Why do we need AI to help us earn more time 24/7?

From the perspective of human being psychology, we’re born to hate repeating and boring jobs. Here I take two scenario examples: Self-driving and marketing A/B testing, to simply and visually elaborate my points.

Self-driving and robots

self driving auto

Driving from my house to the office is my regular routine every day. I still remember my original purpose to buy a car is not only for saving time, but it is also for enjoying driving fun. But I believe that every day you repeat turning left at the same crossing, bump into traffic jams in the same tunnel, and park in the same spot. Acting like a robot causes an uncontrollable tired mood easily from inside to your face. There’s a saying “From enjoying to suffering”.

I’m wondering if I can save time on driving either because I should make use of 60 mins time to think about what ingredients I prepare to cook dinner for my family. Thus I hope an AI robot can replace me 50% of my driving time as mentioned above. So I can enjoy the time and make it meaningful. The answer is obvious now.

Marketing A/B Testing

marketing ab testing

I believe marketing is a kind of science, rather than a round gambling game. This scientific methodology behind is a complex combination. The combination can vary by factors that include weather, consumer, a market economy, weather, politics, technology, etc.

I enjoy thinking, designing and implementing marketing strategies on the channel, product, pricing, etc. I am sure I should and need to spend more time on those. Leveraging digital does facilitate me to transform a product, enhance the user experience, drive sales, and retain customers. But the point is ongoing A/B testing is critical but unlimited in the process, for the purpose to make and prove decisions intelligently. Even though we are passionate about spreadsheet daily monitoring A/B testing results by manual effort, AI is a must-win because of its super fast and accurate calculation.

What AI implications for digital marketing success

Artificial intelligence is the absolute winner in speed and accuracy thanks to its calculation and learning power. It saves time and lowers the frequency of tired mood when we repeat the same work. Thus, it helps us increase efficiency and maintains a passion for a job finally. Working with AI is irreversible.

But AI needs algorithms, and also must need a huge amount of data to learn and grow. Also, what rules (algorithm etc) are set up and raw data fed in AI are mattering the final result heavily. Most importantly, the final purpose is to communicate with human beings and grow business. Thus, the cons of AI correspondently occur, compared with a human being. 

3 needs of machine learning

For digital marketing success, there’re 3 aspects of a human being is the sure winner.

ai vs human

  • Digital marketing is a part of business strategy. With earning more time by AI assistance, we are free from repeating work and focus more on business-level considerations. Thus we could spend more time on transformation, pricing, product, business model, partnership development, and innovation work, for the purpose to maintain, and grow business in existing markets and develop opportunities in new markets. This work is all we originally should do.
  • AI relies on the food from human beings to grow, and needs direction from human beings, in order not to sidetrack the purpose and work in the right direction. As digital marketers, we need to have the capacity of sourcing, categorizing, and qualifying data as a gatekeeper, for the purpose to ensure all the algorithms we set up and data we feed to AI are clear constables, and trackable.
  • AI is able to create BGM, news, video, or standard version banner templates automatically. But nobody likes messages and stories without soul and resonance. Consumers buy because they get convinced and touched at the end. Also if everybody is using the same AI platform, how much-personalized artwork can be created? AI can be in assistance to create great content, but human beings need to be the story creator and tellers. This is critical to winning.

5 Strategies for Leveraging AI in Digital Marketing


1. Intelligent decision by combing your business + digital marketing strategy and AI predictive capacity

Instinctively AI is super powerful in crawling data, processing data, calculating performance, and predicting the result and trends, although the capacity border of AI depends on an algorithm we develop in a platform. Also, it is able to predict by finding relationships among variable elements affecting results. For example, Alibaba SYCM predicts your products in Tmall store revenue would go up 35% on Nov 11th next year. SYCM suggests you stock more switch Zelda special version accessories because Zelda 2 will release in early Nov.

However, AI predicts based on historical data and updated data fed via the latest transactions and other APIs. So how many new competitors would bid 1st ranking to sell your best item? Any new products or substitutes would dilute your market share? As a business owner, you need to plan pricing, product, partnership, and content strategy, instead of just relying on historical data and AI smart suggestions. After all, if all your competitors are using AI and learn similar insight, you would lose cutting edge even though your data assets and customers might be unique. There’s a saying “The weather never knows its mood”.

2. Push-model strategy of digital marketing becomes more important (Programmatic, marketing automation)

Search, an e-commerce marketplace, youtube, and OTA are search-oriented channels. It’s a kind of pull-model strategy of digital marketing because consumers are positive to research information and find products they are interested in. So we need to deploy content marketing, pricing, and product strategy for the purpose to convert more clicks and sales from existing needs from search.

However, the cons of the pull-model strategy of digital marketing are due to its pros. Consumers are looking for referral information or already decided on which brand to pay for. So in a way, they’re aware they need something, rather than get inspired and educated by brand messages. Thus, they would compare reviews and more care about pricing.

On the other hand, the push-model strategy of digital marketing can reach and communicate with your target audience positively. Before AI applied to digital marketing, it was as if you throw a stone to catch fish in the sea. Thanks to AI, you can execute your pull-model strategy at the right moment and right place for the right person. For example through Wechat connected with a CRM/CDP, we can reach Louis and communicate with him personalized ad messages through Tencent’s marketing platform – Programmatic buy. Or AI enables them to feed a message via SMS, App notification, instant messenger, or email channel to Louis with exclusive airport lounge special offers when he’s sucking in Tokyo airport due to flight delay. (Marketing automation)

3. Assign responsibility between your teams and the AI chatbot

We need to understand when end consumers need a human being staff to help. When we browse a website popping up a window in front of us, we might leave messages asking some standard questions like service and pricing to know more about the brand, AI chatbot is fully qualified to fulfill this responsibility, and end consumers feel good because AI robots can reply immediately. When we are looking for a promotional discount on a brand, we ask brand’s WeChat chatbot and AI chatbot immediately feeds us the coupon code, that’s perfect because of the real-time response.

However, when duplicate payments happened via Alipay and we feel worried or pissed off, the AI chatbot can’t resolve this only by replying to answers, because end consumers need an explanation and resolution with a positive and calm tone and voice. AI chatbot’s only approach would cause complaints and a bad user experience.

Thus as business owners, we need to assign what scope of questions the AI chatbot handles, and what scope of questions the sales team and CS team staff should take over. 

4. A/B Testing Is A Must With AI Efficiency

Marketing sense and intuition are lovely and I often love saying “this is my sense from years of experience”. But in fact, this is not a convincing method nowadays when AI is sitting with us.

For example, cost per action and cost per click are two bidding approaches in digital marketing channels, such as SEM, social media, display, etc. Although many platforms still purchase inventory by impression and optimize CPA or CPC etc for advertisers, it’s a significant direction for AI to optimize campaigns for you based on clicks or conversion.

Normally CPA is lower than the actual CPA from the CPC model because it tells AI the ads only reach that audience who are ready to buy. Meanwhile, due to this purpose, web traffic from the CPA model is less than CPC.

However, from my experience, I don’t suggest implementing this perception. I persist and suggest implementing A/B testing with the help of AI, for the purpose to see which actual CPA is lower. If the result is similar, I’ll prefer CPC because sustainable online traffic is one of the key metrics in digital marketing success.

5. Map 1st party data with 2nd and 3rd party to keep updating lookalike prospect list

As an e-commerce store owner, you definitely hope to continue growing my customer in CRM and keep the retention rate stable. As a Vlogger, you do hope more audience would like my video content, as well as my fans, do now. Thanks to AI data mapping super capacity, reaching the right audience is not a problem anymore

Take Facebook for an example, we could connect the Facebook business manager with the salesforce marketing cloud – CRM. Through PII data fed by CRM, the Facebook AI robot can help advertisers to map 1st party data with 2nd party data in the FB database, for the purpose to create a lookalike list. These lookalike audience profiles and interests are similar to our existing customers and fans. Leveraging your existing customer profile to map the same profiles on the market is a key ROI-driven strategy to grow market share and explore new markets

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