There is a shift happening all around us. The retail e-commerce landscape is evolving at a faster pace than anything ever did in the history of commerce.
Traditionally, marketers relied heavily on a few broadcast channels like TV or Print ads and the relationship between the buyer and the seller was one that of loyalty.
Now, not so much. With e-commerce platforms popping up every day in an already flooded market and the reducing attention span of the modern buyer, consumer behaviour is transforming at an unprecedented pace.
And with that transformation, Omnichannel analytics is fast becoming a matter of survival for the seller to tap into consumer’s evolving (or distracted) mind and their buying patterns.
According to a December 2017 report by Statista, an average consumer in the US spent 14.1 hours every month shopping online with heavy users spending thrice as much.
Today, buyers demand much more from the sellers, not only in terms of value but also in terms of a seamless experience. They often jump from one online catalogue to another to find their perfect product and the in-store availability.
What is Omnichannel Analytics and how does it affect a sale?
Let’s say I want to buy a pair of Nike Jordans. Typically, I would google different models, search for authentic reviews, compare prices and check for availability.
Once, I am convinced with a particular pair, I will add the item to my cart before searching for the in-store availability of that specific pair to try them on before making a purchase (comfort being a major value driver for me).
There are also risk areas in the process like the particular model being unavailable at the brick or mortar store. It could negatively impact my experience and more often than not – the seller could bid me goodbye as a customer.
This is exactly where Omnichannel analytics and omnichannel data analytics in particular, come into the picture. A smart omnichannel analytics strategy would identify a potential customer and funnel him/her through a purchase with a precise and focussed campaign effort across online and offline consumer touchpoints.
Marketers must understand and accumulate actionable insights from the digital footprints that a customer leaves all over the internet – while searching for a website or while making a transaction et al – to come up with the right strategy.
What are the key elements of Omnichannel data analytics?
From when a customer identifies the need for a particular product until he/she closes the purchase, there are multiple layers of customer interaction involved in the process.
With Omnichannel analytics, retailers can optimize their digital and offline efforts to enhance their targeting strategy across these layers, which can be broadly classified into four key elements:
1. Identifying Customer Uniqueness
Identifying potential customers and understanding their uniqueness is at the core of Omnichannel analytics.
It is achieved when marketers look at the big data and ask questions like how does a customer interact or transact and across which channels or which segments are the most relevant for a particular product category.
The answers to these questions can help a marketer identify targets with higher potential and predict their behaviour.
Social media is fast emerging as the most potent source of customer identification by tracking their interaction with products and companies.
2. Engaging the customer
Customer engagement determines the quality of customer experience through the lifecycle of a buy-sell transaction.
A market can assess the effectiveness of a customer journey through analytics. These assessments provide key insights into what is working and what is not.
Customer satisfaction and conversions score can help a marketer tweak their strategy for a more seamless experience for the customer.
With time more sellers are using more add-on capabilities like ‘chatbots’ and ‘shopping assistants’ to enhance the overall experience of the customer.
Tracking customer journey through analytics goes a long way in ensuring repeat customers by instilling a sense of loyalty by giving them a smoother experience than their competitors.
3. Predicting consumer behaviour
Predicting consumer behaviour is key to ensuring a high satisfaction delivery to the consumer.
It is also very important in the context of saving marketing efforts, after all, marketers lose a lot of their effort in trying to identify which strategy works the best.
Predicting consumer behaviour takes into account multiple relevant consumer traits such as the intent, the potential, the behaviour and so on. These traits or attributes help the marketers in recognizing the correct channel to display their product to the consumer and tweak offers.
For example, the websites that the customer often visits, the time spent on a specific page, the product that the customer is searching for our buying on other platforms etc are all examples of the attributes that assist predicting behaviour.
4. Delivering Value
All of the above strategies and efforts eventually lead here, to the final delivery to the customer. Delivering value to the customer is a function of having the right product at the right place for the right customer at the right time.
Analytics play a pivotal role in helping sellers come up with corrective strategies to optimize their merchandising, get their supply chain in order, manage their inventory better and augment their in-store operations.
Delivering value is trickier than it might sound. There could be thousands of customer journeys with different levels of performance scores. To identify what works best is key to delivering value to the customer.
How can retailers drive higher sales with Omnichannel analytics?
According to a Boston survey report, 85% of the retailers want to create a holistic platform that enables seamless integration of offline and online activities by combining every function within a company.
Omnichannel predictive analysis does exactly that by letting the retailers build a more targeted strategy, tweak their merchandise assortment for higher profits and deliver higher satisfaction scores on a customers’ path to purchase.
Omnichannel data analytics also allows retailers to ensure a more secure environment to protect their customers from fraudulent activities and minimize their losses.
It leads to reduced costs by optimizing the company’s supply chain and enables more cost-effective modes of shipping.
Going forward, the traditional data models are more likely to end up in wasted marketing efforts and most possibly fail in keeping up with a distracted consumer.
While going Omnichannel analytics could be a baffling undertaking, the returns are high when it comes to keeping up with rapidly changing expectations and behaviours of a modern consumer.
If a company or a seller wants to stay relevant in a fast-changing landscape of commerce, they will eventually have to replace their outdated models with more a more holistic model that addresses everything from identification, distribution, engagement and supply chain to cybersecurity for more seamless customer experience.
The balance of power in this new landscape has shifted towards the consumer and company’s need to earn customers’ goodwill at every touchpoint, lest they want to lose a customer to their competitor forever.
