The vital role of big data and AI in performing and forecasting marketing campaigns

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Today, the wide and diverse world of marketing seems unrecognisable from its various incarnations before the era of interconnectivity and super-fast data.

Where focus groups had to be assembled and used as predictive machines for marketers in days of yore, we now have a considerably more efficient set of tools to play with.

The arrival of Artificial Intelligence (AI) and big data has had a profound effect on a variety of industries worldwide. The wealth of computing power and detailed analysis capable of being provided through this modern form of technology makes it possible to transform a range of professions for the better – and marketing has to be regarded as a key beneficiary.

AI software market revenue is forecast to increase to $120 billion by 2025.

Just a matter of years ago, it could’ve felt a little left-field to explore the range of applications both AI and big data could have in improving your various marketing strategies. But now, it feels more tricky to find an area of the industry that doesn’t seem ripe for this kind of enhancement.

It’s fair to say that modern technology can save a significant amount of time and money for marketers looking to optimise their campaigns and deliver persuasive messages to the right audiences at the right time. But how exactly is this done through the use of AI and big data?

Here’s a deeper look at AI and big data applications within the world of marketing, and how the technologies can help you to forecast your campaigns:

Artificial Intelligence analysis

AI in marketing is continuing to grow exponentially. This form of technology is primarily used as a means of analysing the demographics of audiences alongside the analytics behind a business’ performance online. Predictive analysis has the power to highlight metrics like bounce rates, page visitors, the time spent on specific pages and click-through rates, while AI can help users to make smarter decisions based on such information.

Here, AI helps you to understand and concentrate on the specific areas in which your strategy can work best – or, alternatively, where it needs a little improving.

AI-driven predictive analysis can also interpret scores of data in order to build well-informed predictions for your future engagements too. This technology has the power to identify and investigate previous errors as a way of forming a prediction on how best to prevent the same problems arising in the future. This can help to direct more prospective users to your content and enhance their experience within your pages.

AI can also help you to anticipate how best to utilise your Call-To-Actions as a way of increasing your conversion rates. In fact, according to Ventana Research, as much as 68 per cent of entrepreneurs claimed to have developed a competitive edge with this technology. Furthermore, the consumer goods giant, Unilever, managed to reduce their forecasting errors by as much as 15 per cent while saving millions through the help of this form of analytics.

AI enables companies to assess how specific changes impact conversion rates. For instance, Walmart reported that a decrease in the load time of their website of just 1 second can lead to a 2 per cent increase in conversions – which is translated into millions of dollars.

Predictive analysis that offers concrete data can save companies a ton of energy and resources that would otherwise go into complex A/B testing.

Big data transforming predictive marketing

Big data marketing revenue is expected to hit $103 billion by 2027.

The art of forecasting marketing campaigns is one that seemed unimaginable in a more analogue age. With so many metrics and such difficulty associated with the anticipation of customer behaviour, early tools designed to provide guidance for businesses tended to have a high margin for error – or be wholly misleading in some cases.

MarTech relies on the efficiency of predictive technology today – and you’ll rarely come across a sale online that hasn’t arrived as a direct – or indirect – result of the optimisation of big data.

Predictive marketing is an essential tool for any ambitious eCommerce business, and the technology is driven by the implementation of data science as a means of predicting which marketing actions are more likely to succeed as opposed to the ones that look well-positioned to fall flat.

Although both predictive analysis and predictive marketing sound like similar concentrations within the world of modern marketing, there are some profound differences to keep on top of. In the domain of predictive marketing, the analytics behind a marketing campaign is taken a step further and contains a broader implication. Whereas predictive analysis typically relies on predictive models to provide a clear insight into the future. If you’re looking to really put your marketing strategy to the test and gain some insights into the decisions you’re planning on making – predictive marketing is just the tool for the job.

Based on this information, we can use the example of a scenario to further differentiate between these two analytical interpretations. Here, a predictive marketing expert – typically a data scientist or analyst – collates big data regarding a business from a range of sources and then analyses it alongside the company’s marketing and customer data. This information provides the analyst with a predictive model that’s capable of quantifying the success of a specific marketing campaign.

Changing tides of customer profiling

The marriage of AI and big data can carry plenty of benefits to marketers online. Specifically, in the way, that customer behaviour can be mapped out and even anticipated. Because data has the power to be so sophisticated, the marketing efforts built around this type of technology can potentially bring considerably higher conversion rates.

This is particularly pertinent in developing customer acquisition efforts. AI helps to interpret existing data in a way that can optimise campaigns to target exactly the right customers – and at the right time.

Predictive marketing models can enable marketers to customise their campaigns and acquisition strategized into different sections of an effectively segmented potential customer base, bringing better chances of enabling conversions.

In a similar manner, insights into the future behaviour of customers can help a business to map out its customer retention strategies. When the company knows when a segmented group of potential customers are likely to leave their site, or abandon their cart, the marketing team can design a bespoke retention plan that anticipates this departure and nips it in the bud – so to speak.

The ability that AI holds in effectively interpreting masses of data in order to effectively segment target audiences based on their behaviour can’t be underestimated by marketers. Having the ability to unleash bespoke retention strategies for certain behavioural groups as opposed to a straightforward blanket effort to retain visitors can ultimately pay dividends in successful campaigns.

Discovering better prospects

AI and big data can also optimise the efforts of B2B marketers in helping them to gain more quality leads thanks for the collaborative technical efforts of predictive marketing. The apply this, marketers apply their predictive models through a field of signifiers to interpret – with much better accuracy – which businesses can make for strong potential customers and clients.

Data pertaining to a prospective B2B customer’s company size, type of products, levels of revenue along with other metrics that can even include expansion efforts, management changes and thousands of other variables can be thrown efficiently into a mix to identify the best prospects to target.

Using predictive analysis models, marketers can generate long lists of businesses with suitable behaviours and build a quality database that’s rich in effective prospects.

These efforts can help AI and big data to optimise marketing campaigns for both customer-facing businesses and B2B endeavours in a way that not only saves time but one that can be effectively monitored, analysed and have progress forecasted for the desired duration of a campaign.

Economical applications

Some marketers cite the financial implications associated with this roll-out of modern technology as a drawback. But it’s important to note that the wealth of data available would cost fortunes in employee hours to interpret effectively.

Fundamentally, AI and big data-based solutions offer a level of marketing insight that would otherwise be impossible to gain manually, and with this in mind, the costs can be surprisingly competitive.

Is AI and big data essential to the efficiency of your marketing campaigns? It’s certainly possible for some established companies that are nestled in clear niche markets with a dedicated customer-base to feel that this level of analytics and forecasting is superfluous to their efforts. However, these technologies could be imperative in locating a wealth of new customers based on their interactions with said company online – and must be taken into consideration if there are plans for future growth.

Predictive analytical models can be an excellent tool for forecasting marketing performance based on previous campaigns and have the potential to uncover fresh opportunities for businesses to improve and better understand their customers’ behaviours.

With more adopters looking to technology as a tool for better insights, there’s an emerging risk that those who fail to adapt could be left behind. Fortunately, the more AI and big data solutions have developed, the easier they have become to utilise for marketers. In fact, there are many analytics agencies out there that now offer up scalable solutions for businesses that cater effectively for both large and small organisations.

In the wide world of marketing, time really does mean money, and with the power of AI and big data on hand to anticipate the quality of your campaigning and make accurate predictions towards the success of your marketing strategy – there promises to be plenty of time on the side of early adopters.

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