Navigating the Complexities of Omnichannel Marketing: Expertise, Technology, and Data Strategy

From an organisational standpoint, omnichannel requires a diverse skillset and a range of technologies. Organisations require expertise for each marketing channel, either through in-housing or through agencies. Regardless of choice, the risk of siloing data is present, causing a lack of visibility and accessibility. Issues arise when teams grow too rapidly to meet the demand of marketing across multiple channels or fail to implement the sufficient technological infrastructure needed to support such growth. It is not unusual for organisations to have separate paid search and social media teams, which can lead to disconnects if not managed properly. Choosing to sub-contract media trading to agencies can lead to contractual or political issues where ownership of the data is obscure, or data accessibility is limited, dependent on relationship with the vendors. This is what is known as data isolation where data is siloed.

A clear and concise data strategy is needed to prevent data isolation from occurring. The challenges of data silos arise when the data becomes inaccessible and visibility is restricted, hindering progress in utilising the data for reporting, measurement, and performance analysis. Moreover, integrating this data with other business facets or for AI/ML models becomes an even greater hurdle.

In the realm of advertising, it is easy to fall into the data silo trap. Prominent walled gardens such as Amazon and Google, have strict rules in place. While they provide centralised platforms for your marketing needs, autonomous inventory management, audience management, and reporting, all these features do come at a cost. Firstly, they maintain some degree of control of your data, how your data is created, and the way metrics are attributed can be a black box, so comparing the performance of two campaigns from different channels can be challenging. To overcome this, expertise and an in-depth understanding of how each platform works is required. To make matters worse, marketers are at the mercy of the platforms to continue to support the tools they are using. An example of this is Google’s February 2024 announcement, depreciating reports in the Campaign Manager 360 attribution tab, where tools used by marketers simply disappear. These tools play a critical role in a business’ reporting pipeline, yet they are depreciated sometimes with little warning and a lot of the time with no alternative solution in place.

Overcoming Data Isolation in a Privacy-First World

The restriction of the third-party cookie and the implementation of data security and protection of personal information, through the likes of GDPR in Europe and the California Consumer Privacy Act (CCPA) brings consequences of breaking privacy and trust regulation at a high cost, making it much easier to just put the walls up. There are solutions through data clean rooms, which allow for compliance with privacy regulations, fast analysis of data and some ownership of that data. Much of the benefits depend on the solution and the reliance on the first party cookie, which for many organizations are difficult to acquire. The main issue currently is the isolation of data, with no ability to bring data together from different ecosystems. For example, Google’s Ads Data Hub can be a useful tool for marketers solely operating in Google’s ecosystem but when trying to combine the data to another platform, its uses become greatly limited.

To fulfil the purpose of omnichannel-marketing across all channels, it is vital to bring all data together. Accessible and visible data enables greater understanding of how the overall strategy performed, and how each channel performed individually, providing a platform for implementing statistical analysis like Marketing Mix Modelling (MMM). Furthermore, bringing data together will promote greater collaboration between teams, improving decision making and ensuring data’s recency, accuracy, and credibility.

This naturally leads to the questions; how do we prevent our data from being siloed? How do we get our data out of a data silo. It starts with how you approach the data, and by implementing a data strategy that will definethe rules, processes, roles, and technologies that are best for your organisation, helping your business to stay relevant and competitive by driving growth.

Data Democracy and Governance: Ensuring Access and Integrity in Data Management

Data management, which is all activities that ensure data is visible, accessible, readable, scalable, and secure is needed. The DAMA-DMBOK (Data Management Body Knowledge) Functional Framework provides a structured approach to data management with 10 key aspects, pivoting around data governance. Data governance is the starting point, where rules and roles are defined, providing a stable platform of policies to adhere to. The framework prevents any aspect of data from being overlooked and adhering to the framework ensures data is never isolated.

Figure 1 DAMA-DMBOK Functional Framework

Data management can fall under the trendy, umbrella term, data democracy. Data democracy is freeing data to make it accessible to the people who can garner value from it. It is the opposite of siloed data and while many believe data democratisation is making data available to everybody, it should be data readily available to people who need it and understand it, to the stakeholders who can analyse it, squeeze value out of it and make decisions from it. This can only be done once a data strategy, and a data management framework is in place.

Leveraging Data Granularity and AI for Marketing Excellence

Having control of your data provides access to various levels of granularity, positioning you to effectively integrate machine learning and AI into your business and marketing strategies. Machine learning, particularly deep learning, demands substantial data volumes, and the model’s quality is intrinsically linked to the training dataset’s quality. Addressing these challenges enables businesses to harness machine learning for a multitude of marketing applications, including customer segmentation, bid optimization, and forecasting. With precise and reliable data, marketers - and consequently brands - can gain insights into customer behaviour, enhance ROI, and assess campaign performance through cross-channel measurement.