We explained in a previous article the added value and operation of an enriched in-store appointment system. Implementing such a system requires to bridge the gap between the offline and online environments, that still remains too often siloed. In this article, we take you through the prerequisites that are worth keeping in mind before deploying an advanced appointment feature.
Prerequisite #1 – Responsive and enriched analytics data
Through Customer Relationship Management tools, brands can collect a wealth of structured data in stores about customers’ profiles (name, age, gender, …) and purchase history. But CRM data is only the tip of the iceberg. Before most boutique visits and/or transactions, customers spend a lot of time searching, browsing, contemplating and comparing products online. Capturing data about this online process is precious for brands and could help them remain up-to-date with customers’ expectations. They can do so by implementing online tracking and a webanalytics solution such as Google Analytics or Adobe Analytics.
In particular, Google Analytics has been serving foolproof data for websites in all industries since its introduction in 2005. It goes further than that: with its Enhanced Ecommerce ability and the integration with Google Cloud BigQuery in 2017 via the 360 version, Google allows marketers to easily access and manipulate Analytics data in a few clicks and SQL queries.
For those who are not yet familiar with BigQuery, the solution is a database hosted in the cloud. A subscription to Google Analytics 360, Google Analytics’s paid version, systematically comes with exports of the Analytics data into BigQuery. Once exported, raw hit-level analytics data is available in three versions:
- Daily users’ sessions export, available the next day (D+1)
- Intraday export, available the same day (D) and refreshed at least three times a day (approximately every 8 hours)
- Streaming export, available the same day (D) and refreshed almost in real time (within a few minutes)
“What does it mean for my in-store sales?”, you may ask. Well, it implies that you are able to accurately and instantly know the product interests of your customers based on their online behavior – provided that you can reconcile online and offline data on the go. This can help deliver a tailored service in store and hence maximize your conversion rate, a strategy that is particularly relevant at a time when fewer customers are daring to visit boutiques.
Prerequisite #2 – Customer journey reconciliation
With more and more data being available to brands, reconciliation comes as a major issue. The question is: how can I reconcile real-world records (understand, CRM data) with digital data (understand, Webanalytics data), both collected on different platforms, at different moments and in different ways? The answer stands in two words: “reconciliation key”.
A reconciliation key makes it possible to merge customer journeys across multiple environments. Such a process requires a robust and scalable infrastructure that will process a large number of records, no matter the heterogeneity of the data sources.
Luxury brands are usually able to reconcile online and offline customer journeys quite painlessly. This is no secret: a thoroughly deployed architecture and a well-thought reconciliation methodology are the recipe for success. The reconciliation process relies on a unique ID that should be available in all data sources. In most cases, it is a CRM ID or an email address that has been previously anonymized.
For CRM data, this is quite straightforward as the ID or the email address is natively available for each customer. As regards Analytics data, the reconciliation key must be retrieved as the customer conducts a specific action on the website (an account creation, a purchase, etc).
Prerequisite #3 – Safety measures
The large database of reconciled CRM and Webanalytics data could be exposed to a high degree of privacy risk if no safety measures are implemented. This aspect is not to be neglected. Always keep in mind that a data breach could seriously harm your brand reputation and relationship with customers in the long run.
Cloud providers such as Google share responsibilities of Cloud security with the companies that use the service. Providers implement baseline protections for their platform and the data they process: authentication, access control, data encryption, …
Cloud security is robust but not foolproof. Companies should supplement these with added security measures to bolster cloud services protection and restrict access to sensitive data.
Indeed, part of the threats comes from internal sources. A study showed that 30% of cyber breaches are caused by “negligence or mistakes”, caused by individuals failing to act responsibly or follow procedures. This could take the form of weak passwords, the use of unofficial libraries or the misconfiguration of gateways or firewalls.
In order to maintain a safe architecture and keep away from breaches, here are a few elements that you can implement:
- Educate all employees (with no exception) about safety measures and procedures
- Remain vigilant when transmitting information from your professional computer
- Do not overshare: grant only the minimum access required to you and your colleagues
- Encrypt customer data, even at rest (i.e. when it is inactively stored)
- Safely dispose of customers’ personal information when you don’t need them anymore
- Deploy a password safety procedure (e.g. two-step verification process) to verify the authenticity of any access request
Prerequisite #4 – Budget forecast and change management
Being able to deliver a personalized customer experience, in almost no time between data collection, data processing and data availability in store, requires a strategy and specific resources.
First of all, it is highly recommended using a scalable platform to manage, store and reconcile such large databases. A flexible and cost-effective alternative to traditional on-premise infrastructure is the Cloud, which unlocks significant value for companies that aim at delivering real-time personalized services to each and every customer.
Data processing is strongly impacted: instead of processing large volumes of data on a regular basis for a group of customers, single-customer data is processed every time a customer books an appointment. This has a direct consequence on the cost of such a project. Hence, return on investment has to be tightly monitored, yet it generally remains positive due to an increased conversion rate in stores.