A Marketing Trajectory
For decades the quest for market share has pushed the marketing industry to the forefront of tech evolutions. In fact, marketing has one of the highest adoption rates of new software solutions. Changing consumer expectations, demand for profit and competition forces the marketer into a continual pursuit of change.
Each advancement in marketing is building on the last
Each advancement in marketing builds on rather than conquers the last, creating a complex eco-system of mediums and tools that can benefit small and medium businesses alike - if you know how to use them.
Traditional marketing offers physical and branded touchpoints, and broad targeting. It requires consistency, and can take longer, making is cost prohibitive for many small businesses.
Digital marketing has offered us improved audience targeting, flexible pricing and can integrate on and offline experience.
AI-powered marketing is using predictive analytics for hyper personalisation, enabling brands to maximise customer experience as a differentiator. AI can eliminate the guesswork involved in client interactions, create ads specific to audience segments, and offer deeper insights into customer behaviour.
Artificial Intelligence (AI):
- AI groups relevant people together
- Learns recognition eg. Cat or dog (it's one thing or another)
- Categorises action
- Then, forecasts or recommends an action depending on the later
Digital marketing platforms are using AI to get better at 1-4.
AI's appetite for data is insatiable, demanding shared data, for shared purposes. Once the AI 'learns' using this data, it can speed up marketing processes or run on autopilot. However, the opportunity to deliver content with improved relevance is conflicted by privacy demands. It's a paradox marketers face: users expecting hyper personalised content, while evolving privacy needs make it harder to deliver.
Marketers face a paradox: ad platforms offer opportunity to deliver content with improved relevance, something that customers demand, but are conflicted by evolving privacy demands.
This increase in data means that marketers must work in ever more closely with their tech department or IT experts to ensure sensitive information is secured.
Hiring To Keep Up
It's not enough for marketers to simply champion customer experience (CX), AI is helping us craft it. Market research leaders IDC predicted that by 2020, 50% of digital transformation (DX) initiatives would fail due to the lack of an end-to-end customer experience orchestration service. To keep up, organisations are hiring marketers with a Customer Experience bent (CX) or a heavy CX focus.
The Marketer Is an AI Pioneer
Industry changes are pushing marketers to mold themselves into Leonardo Davincis - with creative and inventive ingenuity that bridges customer needs with AI. We're using AI as a copilot and moving from creator to facilitator - redirecting creativity to take a business from adopter to innovator.
The 2023 marketer
- The voice of the customer, fighting for personlisation at every juncture
- Facilitates more content variables, telling stories that AI can't
- Feeds AI requirements: volume
- Leans harder on using digital platforms AI, and set up campaigns to do so
- Discerning: as AI vendors, platforms and technologies grow, tech stacks are being reevaluated. We need vendors that will be around in the future!
Marketing departments are hiring data scientists and programmers to create data-competent teams. It's creating a race for talent. Entities like Accenture are tapping into neglected pools of talent, upskilling refugees to help fill the gaps. SMEs can upskill their team, take on interns and forge partnerships with AI start-ups.
Market research company IDC forecasts that by 2024, 1 in 4 brands will build shared customer data hubs to deliver connected experiences and reduce data acquisition costs. AI-fueled platforms need education using profiling, data and scenarios.
If a marketer has limited customer profiles, they'll have to leverage platforms that already utilise AI - like Facebook Ads Manager. They may need to expand campaign 'reach' to fast-track their platform's delivery 'learnings' or build profiles using third-party data from the likes of Nielsen research.
In theory, the greater the 'education' we can offer platforms that are using AI, the better optimised outcome for the business, and, ideally, the customer.
How AI is Affecting Digital Marketing Campaigns
aI Is making Ad delivery Initially More expensive for sMEs
AI platforms need time to 'learn'. For small businesses with a low or experimental budget, this time lag can impact their initial platform experiences.
A SME's tight budget tends to limit their campaign reach, which also limits the amount of data the platform's AI model has to 'learn' from. The more data AI has to 'learn from', the faster it 'learns' to improve performance. Larger campaigns tend to have a broader reach, fast-tracking the optimisation period.
You may also be interested in: Should you apply Google Ad auto recommendations?
Over time, it's expected small businesses will benefit from large enterprises already adopting AI, as the AI is expected to improve.
AI is helping make ads more relevant
Irrelevant content is the number one reason consumers disengage or ignore campaigns. Machine learning is helping counteract this by finding patterns based on an individual's behaviour, then delivering more relevant, and thus engaging content to that user.
However, it takes time to create content variables for the digital marketing platforms to deliver, and/or takes time for the digital marketing platforms to discover what content variables are the most engaging for different users. This time lag means that smaller businesses can negate engagement measures in their quest for short-term gains.
Seasoned AI adopters believe that AI is key to market leadership - today and in the future. With many marketing specialists working for small businesses, they need to be able to demonstrate experimentation value to stakeholders. Those that have already taken the plunge are achieving payback in a shorter amount of time.
AI is forcing Marketers To Start Campaign 'Learnings' All Over Again
For years, digital marketing specialists have manually been optimising client's ad delivery on digital marketing platforms. There is some controversy over the ad platforms forcing marketers to discard this optimisation and adopt AI, because it's also forcing them to start from 'scratch'.
Starting again can lead to an initial drop in leads or sales. This drop in sales can create some pretty cranky clients before AI 'learning' accelerates. AI requires consistency so that the algorithm has enough data to 'digest' and optimise campaign delivery. Try not to pull the plug too soon.
AI is creating ethical grey zones
It's not secret that AI presents bias and privacy risks. We can lack insight into how algorithms or advertising tools work, risking compliance with standards like GDPR. The opportunity is digital marketing where we no longer track audiences, we predict them and tech becomes better at managing privacy protection.
AI is Improving Return On Ad Spend (ROAS) over time
AI-powered marketing is boosting the ROI of marketing campaigns - as long as you give the platform the time or data it needs to 'learn'. The world's interconnected data sets have created a behavioural science lab where business has option to hyper-personalise interactions, improving relevance, engagement and thus ROAS. Even electronic billboards are being powered by AI-based delivery systems.
The future of AI-powered marketing
As AI-powered marketing campaigns become mainstream, barriers to entry, like time spent 'learning' is expected to diminish. The time spent 'learning' can increase ad costs, but as enterprises transition, platforms will leverage more data and continue to get smarter, predicting audiences based on already established data feeds. This evolution will enable smaller businesses to get on board, with less risk.