Is Deep Learning A Game Changer For Marketing Analytics?
With the help of big data and analytics, companies are already making sophisticated marketing decisions. But its deep learning that is currently delivering impressive outcomes in AI applications and is expected to be a significant leap forward.
Marketers are using deep learning to develop a thorough knowledge of consumers and prospective clients, take these profiles a step further, mould marketing campaigns and create highly personalized content. The technology that underpins deep learning is becoming increasingly capable of analysing big databases for patterns and insights. Deploying this technology, companies are able to integrate a wide array of databases to discern what consumers want with greater sophistication and analytic power and then leverage that information for marketing advantage.
The Deep Learning Approach
Deep learning adopts an entirely unique approach to determine consumer response and sentiments in three key ways.
- Firstly, it isn’t dependent on a singular, easy-to-interpret equation, but rather relies on a series of linear and non-linear changes, each representing a neural layer linked to the next layer. When there are numerous hidden, intermediate layers, the method is known as deep learning. DL models usually require huge data bases for estimating various parameters.
- Secondly, deep learning methods are assessed based on how accurately they can make predictions from a new data base, not only to fit a pre-existing model. Hence, a DL model is considered good if it can predict well.
- The third thing that sets DL apart from other methods is the structural advantage it offers. Deep learning has the potential to handle high-dimensional data effectively which classical methods fail to do – such as text, images, video, audio and other marketing actions. DL models also have the capability to merge huge databases with small-sized databases.
Role of Deep Learning in Marketing
Consumer behaviours and patterns are undoubtedly changing. Marketers need to be at the top of their game to keep up with these changes if they have to tap into their intended audience. Deep learning, with its basic tenets, can play a significant role in providing in-depth insights about consumer sentiments. Majority of the marketing automation solutions & customer interaction tools are already using some of the deep learning applications. As technology evolves further, DL models will become an integral part of marketing software.
One of the most beneficial applications of deep learning in marketing is its capability to enable ‘hyper personalization. Personalization is already a vital part of marketing as personalized content resonates well with consumers and has a much higher rate of conversion. The rise of the Internet of Things (IoT) is enabling the collection of vast amounts of personal data of individuals. Availability of all this data means marketers will have a number of ways to communicate with consumers on a hyper personalized level and they’ll require deep learning to process it successfully.
Deep learning models also assist in SEO. Since SEO is a data-driven exercise wherein the search engines use their own algorithms to finalize the order in which to rank websites on their results’ pages, this makes it a perfect fit for DL technology. Deep learning can tweak designs and content in real-time to optimize them for SEO. This way marketers do not just save time by doing away with the need to test and experiment manually, but the websites too can automatically adjust themselves to algorithm updates without having to entail an SEO expert.
DL models can easily find patterns which are too complex for humans to identify. Traditional data analytics just test linear patterns and hypotheses such as is this right or wrong/is this true or false. But the marketing world is a lot more complex and there are far more predictive & insightful patterns which can be found. Moreover, deep learning can find patterns that are devoid of human bias or pre-conceived notions.
Deep learning unifies disparate data and empowers organisations to deal with complicated data efficaciously. In the process, the silos that exist within companies are broken down so that all business units can work in tandem for best results.
Summing Up
Despite the costs and challenges involved in implementation, there is much optimism about deep learning’s potential to improve marketing by enabling the analysis of rich databases and experimentation in real-time. The accuracy and insight of deep learning applications make it possible for marketers to drive superior business outcomes by projecting precisely what the company needs to do next to better cater to their customers.
