How to create an effective data-driven commercial strategy in 6 steps?
Having an effective strategy is key to the success of every company. But it is even more essential when we transform our business model, introduce new products, reach new markets, or change the industry of operation. In all of those cases, our organization bears great risks, as many unknown external (as well as internal) factors appear. And without proper preparation, the business entity can lose track of its operations, goals, and objectives, and eventually fail.
On the other hand, the continuously changing demands of the different customer segments motivate constant business evolvement and change.
When it comes to doing business, the increasing number of sales is the factor, which makes the company successful and growing. That is why it is essential to base our strategy exactly on our commercial objectives and align the other business aspects accordingly. In other words, having an effective commercial strategy is crucial for the success of every existing enterprise — no matter of its industry.
But how to create not only a simple commercial strategy but an effective one? As building a strategy is all about decision-making, data analytics is a crucial tool to be used when it comes to planning, and risk assessment. Collecting, analyzing and interpreting data are the processes which give us the basis to build on a strong, justified strategy, based on evidence and experience. What is more, data analytics also support the implementation of the strategy, by identifying trends and tendencies in the operational processes, CX, customer behavior and external factors.
The following 6-step data-driven commercial strategy guide, gives useful insights into what factors to take into account.
1. Market analysis
The first step is to look at the market environment. The collection, analysis, and interpretation of Big Data and Market research-based Data helps to evaluate, and identify potential opportunities and market offering gaps or niches. The trick here is to give purpose to the data before collecting it, so that it provides you with the best possible insights when being analyzed. By doing so, the company will be able to recognize the type of market system (Pure competition, Monopoly, etc.) and decide whether a particular market offers potential opportunity, or not.
Besides, data interpretation can offer us the opportunity to study the competitors, their objectives, promotional channels, supply chain flow, partners, past mistakes, and unsuccessful projects is a key part of the analysis. This way, the organization will be able to learn from extrinsic mistakes and get ideas on how to innovate, differentiate and position its products and services on the market.
Such “data-based overview” will allow the business to efficiently identify trends in the consumer’s behavior as well as their preferences and tastes. This way, it can segment its potential customers in different target groups and even create buyer personas. What is more, the marketing department will gain insights on how to effectively approach the company’s prospects and transform them into paying, loyal customers.
One other great advantage for the organizations, derived from data analytics, is to identify the individual preferences of each. As a result, the business will be able to provide tailored offerings, customized messages and personalized products and services, and gain great competitive advantage.
2. Sales planning
After the environmental analysis and evaluation, ideally, we have identified the right customers and the right market for our company. And now it is time to plan how to sell our products and services in accordance with the information that we already have.
It is essential to choose suitable sales and promotional channels: those can be physical or digital. Fortunately, data integration can help with this task. By analyzing and evaluating the traffic of the two types of channels, we can recognize a tendency in their traffic. Lets say, the conversion rate in our physical store is 3.5% (people who enter the store and actually buy something), yet, the rate in our webshop is 5%. If we integrate this information with data regarding the channels’ revenue for the past few quarters, we can make justified decision on which channel is worth investing in, and which one we should expand.
We can also utilize data analytics to identify which channel fully suits the desires, needs, and wants of our identified target groups. By using insights based on pure data, we can make evidence-based decisions and be sure that we don't take unmeasured risks, but take advantage of the best possible opportunity out there.
Another aspect of sales planning is to estimate the number of future sales for a period of at least 8 months in advance. This estimation can be made most accurately, using data interpretation, based on evidence, tendencies, past experience, competitors observations, or another reference point. This will give the business entity clarity on how to organize its supply chain operations, as well as to determine the number of employees, working in marketing, sales, customer service, and support departments.
3. Operations strategy
This third step is fully based on the previous two, and cannot exist only by itself. It will not make sense to create an operational strategy if we don’t know the market, the consumers, and the estimated sales amounts.
Organizing the operations according to the sales planning is key because we can decide in advance what type of manufacturing strategy to design. MTS, ATO, MTO, or ETO, it is all up to the tastes of our target-groups and the available machine capital in the manufacturing plant.
And if we decide to adopt MTS, we have to think about the cost of warehousing. Do we aim to have product surplus? How will we manage obsolete inventories? When should we produce more? Are we going to use the method of First-in-first-out or Last-in-first-out? Are we going to outsource certain tasks? Do we have to invest in new machines, IoT, Data collector software, or AI?
Answering those questions may appear to be complicated; however, data integration can facilitate the process. By collecting data from sensors, IoT network devices, machines, assembly lines and AI robots, businesses can benefit from machine-learning and predictive analytics. Thus, they gain useful insights, which can be effectively used in the planning and task optimization processes. What is more, in the long run, real-time data analytics can benefit the organization by tracking events, devices, tasks, and processes, as well as instantly identifying appeared operational issues.
To have a well-thought-through operational strategy is crucial because it has a major influence on the total costs of the organization. Every aspect of the supply chain (supplying, manufacturing, warehousing, and distributing) should be custom modified in order to optimize the flow of operations and achieve economies of scale. This way, the business will have more opportunities for cost reduction.
4. Procurement plan
This plan is crucial for cost-optimization and smooth flow of supply chain activities. This is the stage when the organization has to research its potential partners, identify risky alliances, and recognize sustainable business associates.
Having access to specific data analytics insights regarding those potential partners can improve the decision-making process of the management. This way, their decisions will be entirely based on facts and figures, and not on market rumors or pure intuition. As a result, the company is allowed to efficiently segment the different partner groups. Such data-driven insights enable the business entity to prioritize its relationship building, according to the importance of alliances and to establish a strong network of reliable partners.
5. Business planning
The essence of business planning is to integrate the market analysis, sales planning, operations strategy, and procurement plan in one effective organism, working towards growth and business development. Having information about all those different aspects in one place is essential in order to achieve continuous growth, profit increase and cost cut. For this reason, it is crucial for the organization to have access to data-integration software, where it can store, process, categorize, and analyze any data it needs, in real-time. The business can also decide whether to store structured or unstructured data in data-warehouses or data-lakes. Moreover it has the opportunity to choose whether to collect Big Data, Small Data, Dark Data, Smart Data, or Real-time Data, according to the corporate goals and management demands.
Such data integration process will ensure successful decision- making, planning, and risk management processes, based on data- driven, evidence-based insights.
On the other hand, in order to enhance the successful performance of the business, we have to establish a set of guidelines: Organizational culture, Mission, Vision, Values, and Objectives. In addition, setting short-term SMART goals will help to approach the big goals step by step and measure real-time progress. It is essential to track the advance and the development of the company in a measurable, evidence-based way.
In this case, data analytics is a key tool that can both give a broad overview of the progress, and show the ups and downs of the growth curve. It enables the management to identify successful projects, mistakes, tendencies in the supply chain processes, changes in customer behavior, as well as to predict future demands and possible risks.
With the obtained data insights, the organization has the opportunity to prepare a data-driven risk assessment matrix. Taking into account the probability and the level of impact of the various risk factors in the company is essential for effective business planning. This way, the organization will have a clear view of the possible future risks and will be allowed to decide whether to avoid, transfer, mitigate, or exploit them.
6. Laws and regulations
Doing business without complying with the legal rules is considered to be a crime. That is why every commercial strategy needs to be aligned with the specific regulations that apply to any particular market and business.
For example, if a specific company is selling products through its webshop, there are numerous factors to take into consideration. Some of them are establishing Terms and Conditions policy, having intellectual property rights on the domain, and complying with GDPR criteria.
High-quality data-integration software is a must when it comes to regulations, in order to ensure security, and transparency to data storage, analysis and transactions on a global scale. This will prevent any data leaks, faulty analytics and lags in the operational processes.
Both corporations, small private companies, and startups aim to position themselves on the market and increase their sales revenues.
By developing a high-quality data-driven commercial strategy, we draw the path towards our goals, estimate future events, predict possible issues, optimize tasks, and set progress criteria. All of this helps our business to grow in the direction of attracting new customers, retaining loyal ones, and building a strong client network. And in 2019 data analytics give us a great number of possibilities to help ourselves and facilitate this development process.