4 Ways Real-Time Data Improves Customer Service

This article was originally published on RT Insights.

Instant access and 360-degree views of all customer and product data is mandatory to enable customer service representatives to operate more efficiently.

Customer Service

Customer service centers serve as organizational information hubs, resonating with the voices of the customers. They are strategic to an enterprise, as they are often the most recent and most frequent point of contact that the customer has with an organization.

Properly used, customer service centers can satisfy customers and improve retention. They can also drive revenue by cross-selling and upselling. To do this, they must manage the volume of interactions efficiently and control average handle time (AHT). Increasingly, they must achieve this with tighter budgets. Instant access and 360-degree views of all customer and product data is mandatory to enable customer service representatives (CSRs) to operate more efficiently.

With people and information spread across various locations, this task can seem daunting. The right mix of technology can enable customer service centers to overcome these challenges and run at peak performance. Below are four tips for CSRs to manage high volume of interactions:

Improving visibility into real-time customer data

CSRs need visibility into customer data across all contact and interaction points within the enterprise — regardless of location, repository and format. By aggregating all data and providing a single, secure access point to relevant and real-time customer and product information, a unified view of information can be formed to help CSRs respond to customers’ concerns and issues quickly and accurately.

Relieved of the burden of navigating multiple applications to find a single piece of relevant information, CSRs can immediately concentrate on the callers’ concerns and quickly resolve their issues — increasing first call resolution and reducing average handle time to minimize the volume of customer interactions. Automatically providing a unified view of customer information effectively enables the customer service center to improve productivity and reduce operating expenses.

Automating access to relevant information

High attrition has always been a major concern for customer service center managers. Rehiring and retraining costs directly impact the bottom line. More importantly, high turnover rates burden CSRs, affect productivity and hamper the customer service center’s ability to provide quality service.

Automating access to relevant information can help customer service centers lower attrition by minimizing the excessive pressure and stress of the customer service center environment, which is cited as a major reason for attrition.

Leveraging automated analytics on top of customer and product information, customer service center managers can quickly spotlight new products for training and push information out to their CSRs. Simplifying the way CSRs access customer and product information and providing ways for CSRs to easily collaborate and share knowledge reduces CSR stress and consequently turnover. When CSRs have the information needed to answer customer questions and resolve issues confidently, they are much better able to interact comfortably and build close and lasting customer relationships.

Accelerating time to proficiency

CSRs never know what inquiry or problem they will face on the other side of an inbound call. As such, they must be well-versed on the products, services and policies of their organization. Successfully training CSRs is vital to the success of the customer service center. The cost of attrition per CSR is high, with new employees taking up to three months to complete initial training in many industries.

This can be exacerbated as many customer service centers have myriad applications and repositories, such as CRMs, ERPs and external databases, that CSRs must learn to navigate to prepare for and complete a call. The ability to seamlessly connect to these applications and provide a unified view to information greatly reduces training time and cost.

Sharing CSR knowledge

Collaboration capabilities that promote knowledge sharing and retention — even if employees leave — enable the remaining CSRs to maximize and enrich each customer interaction. Enterprise data is continually growing; as a result, CSRs have even more information to learn and retain. In addition, customer service centers are often scattered across far-reaching locations without sufficient support for their distributed organization. A scalable, distributed platform for information access solves this problem and allows data to grow without compromising access or speed for CSRs. They can then concentrate on listening to customer concerns and ensuring complete satisfaction, enhancing the entire customer experience.

Companies that employ the right mix of technology in their customer service centers empower their CSRs to go beyond solving customer issues to being customer champions — listening and responding fittingly to their needs.  By actively listening, CSRs can turn complaints into revenue. By having relevant information consistently and securely available, organizations can react quickly to customer demands, innovate business processes, profile new target markets and formulate ideas for new product features.

Consolidating silos and promoting the quick and easy transfer of information and insight captured in the customer service center across the entire enterprise allows executives to make informed decisions that positively impact the direction of the company.

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Congrès Big Data Paris 2013 – Interview d’Hans Josef Jeanrond

Lors du congrès Big Data 2013 qui s’est tenu les 3 et 4 avril, Hans Josef Jeanrond, Directeur Marketing de Sinequa, a présenté l’intérêt des solutions de search et  des plateformes d’Accès Unifié à l’Information pour traiter et valoriser les données non structurées contenues dans le Big Data.

Retrouvez l’interview :
Big Data 2013

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On the road to Big Data

The Documation trade fair and conference was a resounding success for Sinequa: Visitors came with more concrete projects than in previous years. Conferences were very well attended, and the roundtable discussion on Big Data with Sinequa and partners was crowded, even standing room overflowing into the aisles.

Three major players EMC²; CGI Business Consulting and Sinequa focused on ROI of Big Data projects. They form an alliance to address the challenge of value creation in the various areas and phases of real life Big Data projects.

Philippe Nieuwbourg, specialized journalist, lecturer and book author on Big Data moderated the discussion and contributed his own experience. He provoked people to breathe life into “data cemeteries” that have accumulated in many companies, and extract value from it ion realistic projects.

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La coopération homme-machine distance les « supercalculateurs »

Vous connaissez peut-être la vielle blague sur le supercalculateur à qui on a posé la question « quel est le sens de la vie », et qui sortait (après un long temps de calcul) la réponse « 27 ». Cette blague a été racontée pour illustrer bien des aspects différents  sur des ordinateurs, sur l’interaction homme machine, la philosophie et la vie en général. Ici, je voudrais tirer votre attention sur les questions floues et réponses précises, ou vice-versa, et à l’amélioration de l’interaction entre les hommes et les machines.

Shyam Sankar a donné une conférence intéressante à ce sujet à  TED, sous le titre de « l’ascension de la coopération homme-ordinateur ». Dans son discours il explique pourquoi « résoudre de grands défis (comme arrêter des terroristes ou identifier des tendances émergentes) n’est pas tant une question de trouver le bon algorithme mais plutôt de la bonne relation symbiotique entre calcul d’ordinateurs et créativité humaine ».

Son premier exemple est bien connu mais il mérite d’être raconté encore une fois. C’est l’histoire de deux championnats d’échec de niveau mondial : En 1997, le champion du monde Gary Kasparov perd contre l’ordinateur « Deep Blue » d’IBM. En 2005, dans un championnat d’échec ouvert à tous, dans lequel des hommes peuvent jouer avec des machines comme partenaires, un supercalculateur a été battu par un grand-maître avec un ordinateur portable assez médiocre. Mais à la surprise de tous, le tournoi a été remporté non pas par un grand maître associé à un supercalculateur, mais par deux amateurs avec trois ordinateurs portables assez faibles. Sankar  pense que c’est la façon d’interagir avec leurs machines qui a fait gagner des hommes moyens avec des ordinateurs moyens contre les meilleurs hommes avec les meilleures machines.

Très bien, mais quelle relation avec le Search ou l’Accès unifié à l’information (Unified Information Access UIA) ?

Peut-être la relation est-elle tenue, et peut-être je ne l’exprime pas bien, mais je vous sollicite de m’aider avec votre « puissance symbiotique cerveau-calculateur » pour affiner  mon argument.

Dans l’informatique “classique”, où l’on travaille avec des bases de données, des entrepôts de données (data warehouses), des systèmes de BI, etc. , des questions précises sont posées au (super) calculateur par des gens qui connaissent la structure de leurs données et maitrisent la façon de poser ces questions, et l’ordinateur sort des réponses précises du genre « 27 » ou des tableaux de bord sympathiques qui illustrent des chiffres et même des trends. Mais si vous voulez poser des questions qui vous amènent en dehors des structures de vos données ou de la logique prédéfinie de vos « systèmes décisionnels », vous n’aurez pas de chance.

Le Search, par contre, vous permet de poser des questions floues en langage naturel et il ne vous retournera pas une réponse du type « 27 », mais un ensemble de réponses – des documents ou des entrées d’une base de données – classées dans des catégories (des « facettes ») dans lesquelles vous pouvez naviguer. (Des informations de sources multiples, y inclus des applications métier,  peuvent être agrégés dans une catégorie) Vous pouvez zoomer sur des sous-catégories que votre intelligence humaine reconnait instantanément comme les plus prometteuses. Vous pouvez aussi raffiner votre question suite aux idées que le premier lot de réponses vous aura données. En effet, vous pouvez poser n’importe quelle question que vous voulez sans aucune nécessité de (re) programmer quoi que ce soit. Et dans un ping-pong de trois échanges avec votre solution de Search vous avez de fortes chances de découvrir une réponse que votre supercalculateur avec ses logiciels élaborés n’aurait pas trouvée. Ou peut-être il l’aurait trouvée, mais après quelques milliers de jours-hommes de développement et de mise au point, et des millions d’Euros dépensés pour un matériel de pointe – tout comme Watson a gagné le jeu Jeopardy.

Chez Sinequa, nous aimons penser que nos logiciels sont meilleurs que la moyenne, mais même si vous présumez qu’ils soient tout justes dans la moyenne, l’interaction des utilisateurs avec notre plateforme de Search et d’accès unifié à l’information (Unified Information Access, UIA) se rapproche assez de celle des deux amateurs avec leurs ordinateurs portables qui ont battu le champion de l’échec avec son supercalculateur.

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Man-machine co-operation trumping supercomputers

You may know the old joke about the supercomputer that was fed the question “what is the meaning of life”, and that came up – after long calculations – with the answer “27”. This joke has been told to illustrate many different points about computers, human computer interaction, philosophy, and life in general. Here, I would like to draw your attention to fuzzy questions and precise answers, or vice versa, and to better ways of interacting with computers.

Shyam Sankar gave an interesting talk on the subject at TED, called “The rise of human-computer cooperation”, where he “explains why solving big problems (like catching terrorists or identifying hidden trends) is not a question of finding the right algorithm, but rather the right symbiotic relationship between computation and human creativity”.

His first example is well known but deserves to be retold. It is the story of 2 world-class chess championships: World champion Gary Kasparov losing against IBM’s “Deep Blue” computer in 1997. In 2005, in a free-style chess tournament, in which men and machines could participate as partners, a supercomputer was beaten by a grand master with a relatively week laptop. But to everyone’s surprise, the tournament was not won by a grandmaster with a supercomputer, but by two amateurs using three relatively week laptops. Sankar argues that the way of interacting with their machines helped average men with average machines beat the best men with the best machines.

Now what has that got to do with Search or Unified Information Access?

Well, it may be a long shot and maybe I am not putting it into the most convincing sentences, but perhaps someone can help me by adding their symbiotic brain-and-computer power to my argument.

In “classic” computing, using databases, data warehouses, BI systems, etc., the (super) computer is asked precise questions – by people who understand the structure of their data and the way to ask these precise questions – and the computer comes up with an answer like “27” or a nice dashboard illustrating figures and possibly even trends. If you want to ask a question that takes you outside the structure of the data or the predefined logic of the “decision support” programs, you are out of luck.

Search, on the other hand, allows you to ask fuzzy questions in natural language and it comes up not with a “27”-type answer, but with a set of answers – be they documents or data base records – ordered in categories for you to navigate in. (Categories aggregate information from multiple sources, including business applications.) You can zoom-in on what your human intelligence recognizes as the most promising subcategory. It is easy to refine your question after insight from the first set of answers, indeed to ask any question you like without any need for reprogramming. And in a man-machine Ping-Pong of three exchanges, you have a good chance to discover an answer that your supercomputer with its elaborate programs would not have come up with. Or maybe it would – but with a few thousand man-days of development and “tuning”, and millions spent on top-notch hardware – just like Watson won  Jeopardy.

At Sinequa, we like to think that our software is above average, but even if you assume it to be just average, the interaction of users with our Search and Unified Information Access platform is rather like that of the two amateurs with their laptops who beat the chess champion with his supercomputer.

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